Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models

Graph-Based Multicentroid Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a widely recognized approach for data representation. When it comes to clustering, NMF fails to handle data points located in complex geometries, as each sample cluster is represented by a centroid. In this article, a novel multicentroid-based clustering method called graph-based multicentroid NMF (MCNMF) is proposed. Because the method constructs the neighborhood connection graph between data points and centroids, each data point is represented by adjacent centroids, which preserves the local geometric structure. Second, because the method constructs an undirected connected graph with centroids as nodes, in which the centroids are divided into different centroid clusters, a novel data clustering method based on MCNMF is proposed. In addition, the membership index matrix is reconstructed based on the obtained centroid clusters, which solves the problem of membership identification of the final sample. Extensive experiments conducted on synthetic datasets and real benchmark datasets illustrate the effectiveness of the proposed MCNMF method. Compared with single-centroid-based methods, the MCNMF can obtain the best experimental results.

Proposal Distribution Calibration for Few-Shot Object Detection

Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance, i.e., holistic pre-training on base classes, then partial fine-tuning in a balanced setting with all classes. Since unlabeled instances are suppressed as backgrounds in the base training phase, the learned region proposal network (RPN) is prone to produce biased proposals for novel instances, resulting in dramatic performance degradation. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the region of interest (RoI) head from evolving toward novel classes. In this brief, we introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head by recycling its localization ability endowed in base training and enriching high-quality positive samples for semantic fine-tuning. Specifically, we sample proposals based on the base proposal statistics to calibrate the distribution bias and impose additional localization and classification losses upon the sampled proposals for fast expanding the base detector to novel classes. Experiments on the commonly used Pascal VOC and MS COCO datasets with explicit state-of-the-art performances justify the efficacy of our PDC for FSOD. Code is available at github.com/Bohao-Lee/PDC.

A Robust Coevolutionary Neural-Based Optimization Algorithm for Constrained Nonconvex Optimization

For nonconvex optimization problems, a routine is to assume that there is no perturbation when executing the solution task. Nevertheless, dealing with the perturbation in advance may increase the burden on the system and take up extra time. To remedy this weakness, we propose a robust coevolutionary neural-based optimization algorithm with inherent robustness based on the hybridization between the particle swarm optimization and a class of robust neural dynamics (RND). In this framework, every neural agent guided by the RND supersedes the place of the particle, mutually searches for the optimal solution, and stabilizes itself from different perturbations. The theoretical analysis ensures that the proposed algorithm is globally convergent with probability one. Besides, the effectiveness and robustness of the proposed approach are illustrated by illustrative examples compared with the existing methods. We further apply this proposed algorithm to the source localization and manipulability optimization of the redundant manipulator, simultaneously disposing of perturbation from the internal and exogenous system with satisfactory performance.

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups' static networks, consequently overlooking the longitudinal behavior of a reviewer, thus, the dynamics of coreview relations among reviewers in a group. Hence, these approaches are incapable of excluding outlier reviewers, which are fraudsters intentionally camouflaging themselves in a group and genuine reviewers happen to coreview in fraudster groups. To address this issue, we propose "FGDT," a framework for "fraudster group detection through temporal relations." FGDT first capitalizes on the effectiveness of the HIN-recurrent neural network (RNN) in both reviewers' representation learning while capturing the collaboration between reviewers. The HIN-RNN models the coreview relations of reviewers in a group in a fixed time window of 28 days. We refer to this as spatial relation learning representation to signify the generalizability of this work to other networked scenarios. Then, we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group. In the third step, a graph convolution network (GCN) refines the reviewers' vector representations using these predicted relations. These refined representations are then used to remove outlier reviewers. The average of the remaining reviewers' representation is then fed to a simple fully connected layer to predict if the group is a fraudster group or not. Exhaustive experiments of FGDT showed a 5% (4%), 12% (5%), and 12% (5%) improvement over three of the most recent approaches on precision, recall, and F1-value over the Yelp (Amazon) dataset, respectively.

An Improved Non-Negative Latent Factor Model for Missing Data Estimation via Extragradient-Based Alternating Direction Method

In this article, an improved double factorization-based symmetric and non-negative latent factor (Im-DF-SNLF) model is proposed to make the estimation for missing data in symmetric, high-dimensional, and sparse (SHiDS) matrices. The main idea of the Im-DF-SNLF model is fourfold: 1) considering the data variety in the practical engineering, non-negative latent factors (NLFs) in different cases are considered to better reflect the latent relationships between entries; 2) the l2 -norm regularization and the Lagrangian multiplier technique are simultaneously adopted to handle the overfitting and satisfy the non-negative constraint for latent factors (LFs); 3) the extragradient-based alternating direction (EGAD) method is utilized to accelerate the model training and rigidly guarantee the non-negativity of LFS; and 4) a rigorous proof is provided to show that, under the given assumption that the objective function is smooth and has a Lipschitz continuous gradient, the designed algorithm can find an ϵ -optimal solution within O(1/ϵ) , and the upper bound of the learning rate is given by 1/2. Finally, experimental results on public datasets are given to demonstrate the effectiveness of our proposed Im-DF-SNLF model with EGAD.

A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities

The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder–decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.

A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

With the increasing demand for the dexterity of robotic operation, dexterous manipulation of multi-fingered robotic hands with reinforcement learning is an interesting subject in the field of robotics research. Our purpose is to present a comprehensive review of the techniques for dexterous manipulation with multi-fingered robotic hands, such as the model-based approach without learning in early years, and the latest research and methodologies focused on the method based on reinforcement learning and its variations. This work attempts to summarize the evolution and the state of the art in this field and provide a summary of the current challenges and future directions in a way that allows future researchers to understand this field.