Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation

Heterogeneous Domain Adaptation With Generalized Similarity and Dissimilarity Regularization

Heterogeneous domain adaptation (HDA) aims to address the transfer learning problems where the source domain and target domain are represented by heterogeneous features. The existing HDA methods based on matrix factorization have been proven to learn transferable features effectively. However, these methods only preserve the original neighbor structure of samples in each domain and do not use the label information to explore the similarity and separability between samples. This would not eliminate the cross-domain bias of samples and may mix cross-domain samples of different classes in the common subspace, misleading the discriminative feature learning of target samples. To tackle the aforementioned problems, we propose a novel matrix factorization-based HDA method called HDA with generalized similarity and dissimilarity regularization (HGSDR). Specifically, we propose a similarity regularizer by establishing the cross-domain Laplacian graph with label information to explore the similarity between cross-domain samples from the identical class. And we propose a dissimilarity regularizer based on the inner product strategy to expand the separability of cross-domain labeled samples from different classes. For unlabeled target samples, we keep their neighbor relationship to preserve the similarity and separability between them in the original space. Hence, the generalized similarity and dissimilarity regularization is built by integrating the above regularizers to facilitate cross-domain samples to form discriminative class distributions. HGSDR can more efficiently match the distributions of the two domains both from the global and sample viewpoints, thereby learning discriminative features for target samples. Extensive experiments on the benchmark datasets demonstrate the superiority of the proposed method against several state-of-the-art methods.

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %±2.22 % and 84.8 %±2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.

Guided Discrimination and Correlation Subspace Learning for Domain Adaptation

As a branch of transfer learning, domain adaptation leverages useful knowledge from a source domain to a target domain for solving target tasks. Most of the existing domain adaptation methods focus on how to diminish the conditional distribution shift and learn invariant features between different domains. However, two important factors are overlooked by most existing methods: 1) the transferred features should be not only domain invariant but also discriminative and correlated, and 2) negative transfer should be avoided as much as possible for the target tasks. To fully consider these factors in domain adaptation, we propose a guided discrimination and correlation subspace learning (GDCSL) method for cross-domain image classification. GDCSL considers the domain-invariant, category-discriminative, and correlation learning of data. Specifically, GDCSL introduces the discriminative information associated with the source and target data by minimizing the intraclass scatter and maximizing the interclass distance. By designing a new correlation term, GDCSL extracts the most correlated features from the source and target domains for image classification. The global structure of the data can be preserved in GDCSL because the target samples are represented by the source samples. To avoid negative transfer issues, we use a sample reweighting method to detect target samples with different confidence levels. A semi-supervised extension of GDCSL (Semi-GDCSL) is also proposed, and a novel label selection scheme is introduced to ensure the correction of the target pseudo-labels. Comprehensive and extensive experiments are conducted on several cross-domain data benchmarks. The experimental results verify the effectiveness of the proposed methods over state-of-the-art domain adaptation methods.

A review on extreme learning machine

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Attention Regularized Laplace Graph for Domain Adaptation

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. However, current graph embedding DA methods suffer from two issues: 1). they are only concerned with preservation of the underlying data structures in the embedding and ignore sub-domain adaptation, which requires taking into account intra-class similarity and inter-class dissimilarity, thereby leading to negative transfer; 2). manifold learning is proposed across different feature/label spaces separately, thereby hindering unified comprehensive manifold learning. In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely A ttention R egularized Laplace G raph-based D omain A daptation (ARG-DA), to remedy the aforementioned issues. Specifically, by weighting the importance across different sub-domain adaptation tasks, we propose the A ttention R egularized Laplace Graph for class aware DA, thereby generating the attention regularized DA. Furthermore, using a specifically designed FEEL strategy, our approach dynamically unifies alignment of the manifold structures across different feature/label spaces, thus leading to comprehensive manifold learning. Comprehensive experiments are carried out to verify the effectiveness of the proposed DA method, which consistently outperforms the state of the art DA methods on 7 standard DA benchmarks, i.e., 37 cross-domain image classification tasks including object, face, and digit images. An in-depth analysis of the proposed DA method is also discussed, including sensitivity, convergence, and robustness.

Weighted Correlation Embedding Learning for Domain Adaptation

Domain adaptation leverages rich knowledge from a related source domain so that it can be used to perform tasks in a target domain. For more knowledge to be obtained under relaxed conditions, domain adaptation methods have been widely used in pattern recognition and image classification. However, most of the existing domain adaptation methods only consider how to minimize different distributions of the source and target domains, which neglects what should be transferred for a specific task and suffers negative transfer by distribution outliers. To address these problems, in this paper, we propose a novel domain adaptation method called weighted correlation embedding learning (WCEL) for image classification. In the WCEL approach, we seamlessly integrated correlation learning, graph embedding, and sample reweighting into a unified learning model. Specifically, we extracted the maximum correlated features from the source and target domains for image classification tasks. In addition, two graphs were designed to preserve the discriminant information from interclass samples and neighborhood relations in intraclass samples. Furthermore, to prevent the negative transfer problem, we developed an efficient sample reweighting strategy to predict the target with different confidence levels. To verify the performance of the proposed method in image classification, extensive experiments were conducted with several benchmark databases, verifying the superiority of the WCEL method over other state-of-the-art domain adaptation algorithms.

Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework

The process of retrieving essential information from the dataset is a significant data mining approach, which is specifically termed as data clustering. However, nature-inspired optimizations are designed in recent decades to solve optimization problems, particularly for data clustering complexities. However, the existing methods are not feasible to process with a large amount of data, as the execution time taken by the traditional approaches is larger. Hence, an efficient and optimal data clustering scheme is designed using the devised Fractional Sail Fish-Sparse Fuzzy C-Means + Particle Whale optimization (FSF-Sparse FCM + PWO) based MapReduce Framework (MRF) to process high dimensional data. Theproposed FSF-Sparse FCM is designed by the integration of Sail Fish Optimization (SFO) with fractional concept and Sparse FCM. The proposed MRF poses two functions, such as the mapper function and reducer function to perform the process of data clustering. Moreover, the proposed FSF-Sparse FCM is employed in the mapper phase to compute the cluster centroids, and thereby the intermediate data is generated. The intermediate data is tuned in the reducer phase using Particle Whale Optimization (PWO), which is the integration of Particle Swarm Optimization (PSO) and Whale optimization algorithm (WOA). Accordingly, the optimal cluster centroid is computed at the reducer phase using the objective function based on DB-Index. The proposed FSF-Sparse FM + PWO obtained the highest accuracy of 0.903 and lowest DB-Index of 39.07.

Transfer Adaptation Learning: A Decade Survey

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic-related but distribution different source domain. It is an energetic research field of increasing influence and importance, which is presenting a blowout publication trend. This article surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of TAL being created by researchers are identified, i.e., instance reweighting adaptation, feature adaptation, classifier adaptation, deep network adaptation, and adversarial adaptation, which are beyond the early semisupervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges, and understudied issues (universality, interpretability, and credibility) to be broken in the field toward generalizable representation in open-world scenarios.

Self-Supervised Multi-Category Counting Networks for Automatic Check-Out

The practical task of Automatic Check-Out (ACO) is to accurately predict the presence and count of each product in an arbitrary product combination. Beyond the large-scale and the fine-grained nature of product categories as its main challenges, products are always continuously updated in realistic check-out scenarios, which is also required to be solved in an ACO system. Previous work in this research line almost depends on the supervisions of labor-intensive bounding boxes of products by performing a detection paradigm. While, in this paper, we propose a Self-Supervised Multi-Category Counting (S2MC2) network to leverage the point-level supervisions of products in check-out images to both lower the labeling cost and be able to return ACO predictions in a class incremental setting. Specifically, as a backbone, our S2MC2 is built upon a counting module in a class-agnostic counting fashion. Also, it consists of several crucial components including an attention module for capturing fine-grained patterns and a domain adaptation module for reducing the domain gap between single product images as training and check-out images as test. Furthermore, a self-supervised approach is utilized in S2MC2 to initialize the parameters of its backbone for better performance. By conducting comprehensive experiments on the large-scale automatic check-out dataset RPC, we demonstrate that our proposed S2MC2 achieves superior accuracy in both traditional and incremental settings of ACO tasks over the competing baselines.

AdvKin: Adversarial Convolutional Network for Kinship Verification

Kinship verification in the wild is an interesting and challenging problem. The goal of kinship verification is to determine whether a pair of faces are blood relatives or not. Most previous methods for kinship verification can be divided as handcrafted features-based shallow learning methods and convolutional neural network (CNN)-based deep-learning methods. Nevertheless, these methods are still facing the challenging task of recognizing kinship cues from facial images. The reason is that the family ID information and the distribution difference of pairwise kin-faces are rarely considered in kinship verification tasks. To this end, a family ID-based adversarial convolutional network (AdvKin) method focused on discriminative Kin features is proposed for both small-scale and large-scale kinship verification in this article. The merits of this article are four-fold: 1) for kin-relation discovery, a simple yet effective self-adversarial mechanism based on a negative maximum mean discrepancy (NMMD) loss is formulated as attacks in the first fully connected layer; 2) a pairwise contrastive loss and family ID-based softmax loss are jointly formulated in the second and third fully connected layer, respectively, for supervised training; 3) a two-stream network architecture with residual connections is proposed in AdvKin; and 4) for more fine-grained deep kin-feature augmentation, an ensemble of patch-wise AdvKin networks is proposed (E-AdvKin). Extensive experiments on 4 small-scale benchmark KinFace datasets and 1 large-scale families in the wild (FIW) dataset from the first Large-Scale Kinship Recognition Data Challenge, show the superiority of our proposed AdvKin model over other state-of-the-art approaches.

Effective Visual Domain Adaptation via Generative Adversarial Distribution Matching

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM's superiority in image classification across domains.

Capped ℓ1-norm regularized least squares classification with label noise

Since label noise can hurt the performance of supervised learning (SL), how to train a good classifier to deal with label noise is an emerging and meaningful topic in machine learning field. Although many related methods have been proposed and achieved promising performance, they have the following drawbacks: (1) They can lead to data waste and even performance degradation if the mislabeled instances are removed; and (2) the negative effect of the extremely mislabeled instances cannot be completely eliminated. To address these problems, we propose a novel method based on the capped ℓ1 norm and a graph-based regularizer to deal with label noise. In the proposed algorithm, we utilize the capped ℓ1 norm instead of the ℓ1 norm. The used norm can inherit the advantage of the ℓ1 norm, which is robust to label noise to some extent. Moreover, the capped ℓ1 norm can adaptively find extremely mislabeled instances and eliminate the corresponding negative influence. Additionally, the proposed algorithm makes full use of the mislabeled instances under the graph-based framework. It can avoid wasting collected instance information. The solution of our algorithm can be achieved through an iterative optimization approach. We report the experimental results on several UCI datasets that include both binary and multi-class problems. The results verified the effectiveness of the proposed algorithm in comparison to existing state-of-the-art classification methods.

Domain Adaptation by Joint Distribution Invariant Projections

Domain adaptation addresses the learning problem where the training data are sampled from a source joint distribution (source domain), while the test data are sampled from a different target joint distribution (target domain). Because of this joint distribution mismatch, a discriminative classifier naively trained on the source domain often generalizes poorly to the target domain. In this paper, we therefore present a Joint Distribution Invariant Projections (JDIP) approach to solve this problem. The proposed approach exploits linear projections to directly match the source and target joint distributions under the L2-distance. Since the traditional kernel density estimators for distribution estimation tend to be less reliable as the dimensionality increases, we propose a least square method to estimate the L2-distance without the need to estimate the two joint distributions, leading to a quadratic problem with analytic solution. Furthermore, we introduce a kernel version of JDIP to account for inherent nonlinearity in the data. We show that the proposed learning problems can be naturally cast as optimization problems defined on the product of Riemannian manifolds. To be comprehensive, we also establish an error bound, theoretically explaining how our method works and contributes to reducing the target domain generalization error. Extensive empirical evidence demonstrates the benefits of our approach over state-of-the-art domain adaptation methods on several visual data sets.

Locality Preserving Joint Transfer for Domain Adaptation

Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly labeled target domain. A majority of existing works transfer the knowledge at either feature level or sample level. Recent studies reveal that both of the paradigms are essentially important, and optimizing one of them can reinforce the other. Inspired by this, we propose a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection. During the knowledge transfer, we also take the local consistency between the samples into consideration so that the manifold structures of samples can be preserved. At last, we deploy label propagation to predict the categories of new instances. Notably, our approach is suitable for both homogeneous- and heterogeneous-domain adaptations by learning domain-specific projections. Extensive experiments on five open benchmarks, which consist of both standard and large-scale datasets, verify that our approach can significantly outperform not only conventional approaches but also end-to-end deep models. The experiments also demonstrate that we can leverage handcrafted features to promote the accuracy on deep features by heterogeneous adaptation.

Class-specific Reconstruction Transfer Learning for Visual Recognition Across Domains

Subspace learning and reconstruction have been widely explored in recent transfer learning work. Generally, a specially designed projection and reconstruction transfer functions bridging multiple domains for heterogeneous knowledge sharing are wanted. However, we argue that the existing subspace reconstruction based domain adaptation algorithms neglect the class prior, such that the learned transfer function is biased, especially when data scarcity of some class is encountered. Different from those previous methods, in this paper, we propose a novel class-wise reconstruction-based adaptation method called Class-specific Reconstruction Transfer Learning (CRTL), which optimizes a well modeled transfer loss function by fully exploiting intra-class dependency and inter-class independency. The merits of the CRTL are three-fold. 1) Using a class-specific reconstruction matrix to align the source domain with the target domain fully exploits the class prior in modeling the domain distribution consistency, which benefits the cross-domain classification. 2) Furthermore, to keep the intrinsic relationship between data and labels after feature augmentation, a projected Hilbert-Schmidt Independence Criterion (pHSIC), that measures the dependency between data and label, is first proposed in transfer learning community by mapping the data from raw space to RKHS. 3) In addition, by imposing low-rank and sparse constraints on the class-specific reconstruction coefficient matrix, the global and local data structure that contributes to domain correlation can be effectively preserved. Extensive experiments on challenging benchmark datasets demonstrate the superiority of the proposed method over state-of-the-art representation-based domain adaptation methods. The demo code is available in https://github.com/wangshanshanCQU/CRTL.

Deep Cascade Model based Face Recognition: When Deep-layered Learning Meets Small Data

Sparse representation based classification (SRC), nuclear-norm matrix regression (NMR), and deep learning (DL) have achieved a great success in face recognition (FR). However, there still exist some intrinsic limitations among them. SRC and NMR based coding methods belong to one-step model, such that the latent discriminative information of the coding error vector cannot be fully exploited. DL, as a multi-step model, can learn powerful representation, but relies on large-scale data and computation resources for numerous parameters training with complicated back-propagation. Straightforward training of deep neural networks from scratch on small-scale data is almost infeasible. Therefore, in order to develop efficient algorithms that are specifically adapted for small-scale data, we propose to derive the deep models of SRC and NMR. Specifically, in this paper, we propose an end-to-end deep cascade model (DCM) based on SRC and NMR with hierarchical learning, nonlinear transformation and multi-layer structure for corrupted face recognition. The contributions include four aspects. First, an end-to-end deep cascade model for small-scale data without back-propagation is proposed. Second, a multi-level pyramid structure is integrated for local feature representation. Third, for introducing nonlinear transformation in layer-wise learning, softmax vector coding of the errors with class discrimination is proposed. Fourth, the existing representation methods can be easily integrated into our DCM framework. Experiments on a number of small-scale benchmark FR datasets demonstrate the superiority of the proposed model over state-of-the-art counterparts. Additionally, a perspective that deep-layered learning does not have to be convolutional neural network with back-propagation optimization is consolidated. The demo code is available in https://github.com/liuji93/DCM.

Learning Discriminant Direction Binary Palmprint Descriptor

Palmprint directions have been proved to be one of the most effective features for palmprint recognition. However, most existing direction-based palmprint descriptors are hand-craft designed and require strong prior knowledge. In this paper, we propose a discriminant direction binary code (DDBC) learning method for palmprint recognition. Specifically, for each palmprint image, we first calculate the convolutions of the direction-based templates and palmprint and form the informative convolution difference vectors by computing the convolution difference between the neighboring directions. Then, we propose a simple yet effective model to learn feature mapping functions that can project these convolution difference vectors into DDBCs. For all training samples: (1) the variance of the learned binary codes is maximized; (2) the intra-class distance of the binary codes is minimized; and (3) the inter-class distance of the binary codes is maximized. Finally, we cluster the block-wise histograms of DDBC forming the discriminant direction binary palmprint descriptor for palmprint recognition. The experimental results on four challenging contactless palmprint databases clearly demonstrate the effectiveness of the proposed method.

Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

Domain Space Transfer Extreme Learning Machine for Domain Adaptation

Extreme learning machine (ELM) has been applied in a wide range of classification and regression problems due to its high accuracy and efficiency. However, ELM can only deal with cases where training and testing data are from identical distribution, while in real world situations, this assumption is often violated. As a result, ELM performs poorly in domain adaptation problems, in which the training data (source domain) and testing data (target domain) are differently distributed but somehow related. In this paper, an ELM-based space learning algorithm, domain space transfer ELM (DST-ELM), is developed to deal with unsupervised domain adaptation problems. To be specific, through DST-ELM, the source and target data are reconstructed in a domain invariant space with target data labels unavailable. Two goals are achieved simultaneously. One is that, the target data are input into an ELM-based feature space learning network, and the output is supposed to approximate the input such that the target domain structural knowledge and the intrinsic discriminative information can be preserved as much as possible. The other one is that, the source data are projected into the same space as the target data and the distribution distance between the two domains is minimized in the space. This unsupervised feature transformation network is followed by an adaptive ELM classifier which is trained from the transferred labeled source samples, and is used for target data label prediction. Moreover, the ELMs in the proposed method, including both the space learning ELM and the classifier, require just a small number of hidden nodes, thus maintaining low computation complexity. Extensive experiments on real-world image and text datasets are conducted and verify that our approach outperforms several existing domain adaptation methods in terms of accuracy while maintaining high efficiency.

Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation

In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e., nonindependent identical distribution). Maximum mean discrepancy (MMD), as a domain discrepancy metric, has achieved promising performance in unsupervised domain adaptation (DA). We argue that the MMD-based DA methods ignore the data locality structure, which, up to some extent, would cause the negative transfer effect. The locality plays an important role in minimizing the nonlinear local domain discrepancy underlying the marginal distributions. For better exploiting the domain locality, a novel local generative discrepancy metric-based intermediate domain generation learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed in this paper. The merits of the proposed MCTL are fourfold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and DA is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric is presented, such that both the global and local discrepancies can be effectively and positively reduced; and 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario. Experiments on a number of benchmark visual transfer tasks demonstrate the superiority of the proposed MC guided generative transfer method, by comparing with the other state-of-the-art methods. The source code is available in https://github.com/wangshanshanCQU/MCTL.

Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.

Multi-Domain & Multi-Task Learning for Human Action Recognition

Domain-invariant (view-invariant & modalityinvariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain & multi-task learning (MDMTL) method to (1) extract domain-invariant information for multi-view and multi-modal action representation and (2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view & multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multitask learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset, the MSR Daily Activity 3D (DailyActivity3D) dataset, and the Multi-modal & Multi-view & Interactive (M2I) dataset, which is the most recent and largest multi-view and multi-model action recognition dataset, demonstrate the superiority of MDMTL over the state-of-the-art approaches.

A Fast Projection-Based Algorithm for Clustering Big Data

With the fast development of various techniques, more and more data have been accumulated with the unique properties of large size (tall) and high dimension (wide). The era of big data is coming. How to understand and discover new knowledge from these data has attracted more and more scholars' attention and has become the most important task in data mining. As one of the most important techniques in data mining, clustering analysis, a kind of unsupervised learning, could group a set data into objectives(clusters) that are meaningful, useful, or both. Thus, the technique has played very important role in knowledge discovery in big data. However, when facing the large-sized and high-dimensional data, most of the current clustering methods exhibited poor computational efficiency and high requirement of computational source, which will prevent us from clarifying the intrinsic properties and discovering the new knowledge behind the data. Based on this consideration, we developed a powerful clustering method, called MUFOLD-CL. The principle of the method is to project the data points to the centroid, and then to measure the similarity between any two points by calculating their projections on the centroid. The proposed method could achieve linear time complexity with respect to the sample size. Comparison with K-Means method on very large data showed that our method could produce better accuracy and require less computational time, demonstrating that the MUFOLD-CL can serve as a valuable tool, at least may play a complementary role to other existing methods, for big data clustering. Further comparisons with state-of-the-art clustering methods on smaller datasets showed that our method was fastest and achieved comparable accuracy. For the convenience of most scholars, a free soft package was constructed.

2D Direction Histogram-Based Rényi Entropic Multilevel Thresholding

2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Rényi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Rényi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrate the efficacy of REABCMT, three versions of this method are compared in terms of computation time and optimal threshold values. In addition, the segmentation performance of REABCMT is also evaluated by comparing it with two other methods to show its effectiveness. Moreover, in order to evaluate the efficiency and stability of using the ABC algorithm in the search for threshold values, genetic algorithm (GA) and particle swarm optimization (PSO), two common optimization algorithms, are also compared with it.