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Scalable linear visual feature learning via online parallel nonnegative matrix factorization

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dc.contributor.authorZhao, X.
dc.contributor.authorLi, X.
dc.contributor.authorZhang, Z.
dc.contributor.authorShen, C.
dc.contributor.authorZhuang, Y.
dc.contributor.authorGao, L.
dc.contributor.authorLi, X.
dc.date.issued2016
dc.description.abstractVisual feature learning, which aims to construct an effective feature representation for visual data, has a wide range of applications in computer vision. It is often posed as a problem of nonnegative matrix factorization (NMF), which constructs a linear representation for the data. Although NMF is typically parallelized for efficiency, traditional parallelization methods suffer from either an expensive computation or a high runtime memory usage. To alleviate this problem, we propose a parallel NMF method called alternating least square block decomposition (ALSD), which efficiently solves a set of conditionally independent optimization subproblems based on a highly parallelized fine-grained grid-based blockwise matrix decomposition. By assigning each block optimization subproblem to an individual computing node, ALSD can be effectively implemented in a MapReduce-based Hadoop framework. In order to cope with dynamically varying visual data, we further present an incremental version of ALSD, which is able to incrementally update the NMF solution with a low computational cost. Experimental results demonstrate the efficiency and scalability of the proposed methods as well as their applications to image clustering and image retrieval.
dc.description.statementofresponsibilityXueyi Zhao, Xi Li, Zhongfei Zhang, Chunhua Shen, Yueting Zhuang, Lixin Gao and Xuelong Li
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2016; 27(12):2628-2642
dc.identifier.doi10.1109/TNNLS.2015.2499273
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.orcidShen, C. [0000-0002-8648-8718]
dc.identifier.urihttp://hdl.handle.net/2440/108835
dc.language.isoen
dc.publisherIEEE
dc.relation.grant2012CB316400
dc.relation.grant2015CB352300
dc.relation.grantCNS-1217284
dc.relation.grantCCF-1018114
dc.rights© 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
dc.source.urihttps://doi.org/10.1109/tnnls.2015.2499273
dc.subjectFeature learning; nonnegative matrix factorization (NMF); online algorithm; parallel computing
dc.titleScalable linear visual feature learning via online parallel nonnegative matrix factorization
dc.typeJournal article
pubs.publication-statusPublished

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