Efficient Generalized Fused Lasso and Its Applications

Adaptive Diffusion Pairwise Fused Lasso LMS Algorithm Over Networks

The topic of identification for sparse vector in a distributed way has triggered great interest in the area of adaptive filtering. Grouping components in the sparse vector has been validated to be an efficient way for enhancing identification performance for sparse parameter. The technique of pairwise fused lasso, which can promote similarity between each possible pair of nonnegligible components in the sparse vector, does not require that the nonnegligible components have to be distributed in one or multiple clusters. In other words, the nonnegligible components may be randomly scattered in the unknown sparse vector. In this article, based on the technique of pairwise fused lasso, we propose the novel pairwise fused lasso diffusion least mean-square (PFL-DLMS) algorithm, to identify sparse vector. The objective function we construct consists of three terms, i.e., the mean-square error (MSE) term, the regularizing term promoting the sparsity of all components, and the regularizing term promoting the sparsity of difference between each pair of components in the unknown sparse vector. After investigating mean stability condition of mean-square behavior in theoretical analysis, we propose the strategy of variable regularizing coefficients to overcome the difficulty that the optimal regularizing coefficients are usually unknown. Finally, numerical experiments are conducted to verify the effectiveness of the PFL-DLMS algorithm in identifying and tracking sparse parameter vector.

Connectivity-informed adaptive regularization for generalized outcomes

One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV- individuals.

End-to-End Text-to-Image Synthesis with Spatial Constrains

Although the performance of automatically generating high-resolution realistic images from text descriptions has been significantly boosted, many challenging issues in image synthesis have not been fully investigated, due to shapes variations, viewpoint changes, pose changes, and the relations of multiple objects. In this article, we propose a novel end-to-end approach for text-to-image synthesis with spatial constraints by mining object spatial location and shape information. Instead of learning a hierarchical mapping from text to image, our algorithm directly generates multi-object fine-grained images through the guidance of the generated semantic layouts. By fusing text semantic and spatial information into a synthesis module and jointly fine-tuning them with multi-scale semantic layouts generated, the proposed networks show impressive performance in text-to-image synthesis for complex scenes. We evaluate our method both on single-object CUB dataset and multi-object MS-COCO dataset. Comprehensive experimental results demonstrate that our method significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.

Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework

Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this paper, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a "CCA-type" formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem. We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy data set and a more advanced simulated imaging genomics data set in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms data and functional magnetic resonance imaging data from a population of adolescents ( subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance analysis of the resulting features that allow us to carefully extract brain regions and genes linked to learning and cognitive ability.

Feature Selection and Transfer Learning for Alzheimer’s Disease Clinical Diagnosis

Background and Purpose: A majority studies on diagnosis of Alzheimer’s Disease (AD) are based on an assumption: the training and testing data are drawn from the same distribution. However, in the diagnosis of AD and mild cognitive impairment (MCI), this identical-distribution assumption may not hold. To solve this problem, we utilize the transfer learning method into the diagnosis of AD. Methods: The MR (Magnetic Resonance) images were segmented using spm-Dartel toolbox and registrated with Automatic Anatomical Labeling (AAL) atlas, then the gray matter (GM) tissue volume of the anatomical region were computed as characteristic parameter. The information gain was introduced for feature selection. The TrAdaboost algorithm was used to classify AD, MCI, and normal controls (NC) data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, meanwhile, the “knowledge” learned from ADNI was transferred to AD samples from local hospital. The classification accuracy, sensitivity and specificity were calculated and compared with four classical algorithms. Results: In the experiment of transfer task: AD to MCI, 177 AD and 40NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data, the highest accuracy achieved 85.4%, higher than the other four classical algorithms. Meanwhile, feature selection that is based on information gain reduced the features from 90 to 7, controlled the redundancy efficiently. In the experiment of transfer task: ADNI to local hospital data, the highest accuracy achieved 93.7%, and the specificity achieved 100%. Conclusions: The experimental results showed that our algorithm has a clear advantage over classic classification methods with higher accuracy and less fluctuation.

Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease

OBJECTIVE

Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from Magnetic Resonance Imaging (MRI) measures. Recently, the multi-task feature learning (MTFL) methods have been widely studied to predict cognitive outcomes and select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, the existing MTFL assumes the correlation among all the tasks is uniform, and the task relatedness is modeled by encouraging a common subset of features with neglecting the inherent structure of tasks and MRI features.

METHODS

In this paper, we proposed a generalized fused group lasso (GFGL) regularization to model the underlying structures, involving (1) a graph structure within tasks and (2) a group structure among the image features. Then, we present a multi-task learning framework (called GFGL-MTFL), combining the ℓ_{2, 1}-norm with the GFGL regularization, to model the flexible structures.

RESULTS

Through empirical evaluation and comparison with different baseline methods and the state-of-the-art MTL methods on data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we illustrate that the proposed GFGL-MTFL method outperforms other methods in terms of both Mean Squared Error (nMSE) and weighted correlation coefficient (wR). Improvements are statistically significant for most scores (tasks).

CONCLUSIONS

The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods, and the estimated correlation of the cognitive functions and the identification of cognition relevant imaging markers are clinically and biologically meaningful.