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A systematic review of structured sparse learning

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Frontiers of Information Technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： sparse learning Structured sparse learning Structured regularization

Robust object tracking with RGBD-based sparse learning

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Frontiers of Information Technology & Electronic Engineering 2017年第7期18卷 989-1001页

作者： Zi-ang MA Zhi-yu XIANG College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of lnformation Network Technology Hangzhou 310027 China

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.

关键词： Object tracking sparse learning Depth view Occlusion templates Occlusion detection

Building trust networks in the absence of trust relations

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Frontiers of Information Technology & Electronic Engineering 2017年第10期18卷 1591-1600页

作者： Xin WANG Ying WANG Jian-hua GUO College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun 130012 China School of Mathematics and Statistics Northeast Normal University Changchun 130024 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media *** issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.

关键词： Trust network sparse learning Homophily effect Interaction behaviors

Protein Phosphorylation Site Prediction via Feature Discovery Support Vector Machine

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Tsinghua Science and Technology 2012年第6期17卷 638-644页

作者： Yi Shi Bo Yuan Guohui Lin Dale Schuurmans Department of Computing Science University of Alberta Edmonton T6G 2E8 Canada Department of Computer Science Shanghai Jiao Tong University Shanghai 200240 China

Protein phosphorylation/dephosphorylation is the central mechanism of post-translational modification which regulates cellular responses and phenotypes. Due to the efficiency and resource constraints of the in vivo methods for identifying phosphorylation sites, there is a strong motivation to computationally predict potential phosphorylation sites. In this work, we propose to use a unique set of features to represent the peptides surrounding the amino acid sites of interest and use feature selection support vector machine to predict whether the serine/threonine sites are potentially phosphorylable, as well as selecting important features that may lead to phosphorylation. Experimental results indicate that the new features and the prediction method can more effectively predict protein phosphorylation sites than the existing state of the art methods. The features selected by our prediction model provide biological insights to the in vivo phosphorylation.

关键词： protein phosphorylation support vector machine sparse learning feature selection position-specificscoring matrix

Denoising enabled channel estimation for underwater acoustic communications:A sparsity-aware model-driven learning approach

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Intelligent and Converged Networks 2023年第1期4卷 1-14页

作者： Sicong Liu Younan Mou Xianyao Wang Danping Su Ling Cheng School of Informatics Xiamen UniversityXiamen 361005China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the Information Engineering University of the WitwatersrandJohannesburg 2050South Africa

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions,including frequency-selective property,high relative mobility,long propagation latency,and intensive ambient noise,*** this end,a deep unfolding neural network based approach is proposed,in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning,and a scheme based on the Sparsity-Aware DNN(SA-DNN)for UAC estimation is proposed to improve the estimation ***,we propose a Denoising Sparsity-Aware DNN(DeSA-DNN)based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network,so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further *** results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision,pilot overhead,and robustness,particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.

关键词： Orthogonal Frequency Division Multiplexing(OFDM) Underwater Acoustic Communications(UAC) sparse recovery deep learning sparse learning denoising approximate message passing

Codimensional matrix pairing perspective of BYY harmony learning:hierarchy of bilinear systems,joint decomposition of data-covariance,and applications of network biology

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Frontiers of Electrical and Electronic Engineering in China 2011年第1期6卷 86-119页

作者： Lei XU Department of Computer Science and Engineering The Chinese University of Hong KongHong KongChina

One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model *** a complementary role,the paper provides further insights from another perspective that a co-dimensional matrix pair(shortly co-dim matrix pair)forms a building unit and a hierarchy of such building units sets up the BYY *** BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair,which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse *** updating typical algorithms of factor analysis(FA),binary FA(BFA),binary matrix factorization(BMF),and nonnegative matrix factorization(NMF)to share such a mechanism,we are also led to(a)a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA(LFA);(b)an alternative formulation of graph Laplacian based linear manifold learning;(c)a codecomposition of data and covariance for learning regularization and data integration;and(d)a co-dim matrix pair based generalization of temporal FA and state space ***,with help of a co-dim matrix pair in Hadamard product,we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space ***,we address that these advances provide with new tools for network biology studies,including learning transcriptional regulatory,Protein-Protein Interaction network alignment,and network integration.

关键词： Bayesian Ying-Yang(BYY)harmony learning automatic model selection bi-linear stochastic system co-dimensional matrix pair sparse learning denoise embedded Gaussian mixture de-noise embedded local factor analysis(LFA) bi-clustering manifold learning temporal factor analysis(TFA) semi-blind learning attributed graph matching generalized linear model(GLM) gene transcriptional regulatory network alignment network integration