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Type: Journal article
Title: Scalable linear visual feature learning via online parallel nonnegative matrix factorization
Author: Zhao, X.
Li, X.
Zhang, Z.
Shen, C.
Zhuang, Y.
Gao, L.
Li, X.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2016; 27(12):2628-2642
Publisher: IEEE
Issue Date: 2016
ISSN: 2162-237X
Statement of
Xueyi Zhao, Xi Li, Zhongfei Zhang, Chunhua Shen, Yueting Zhuang, Lixin Gao and Xuelong Li
Abstract: Visual 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.
Keywords: Feature learning; nonnegative matrix factorization (NMF); online algorithm; parallel computing
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TNNLS.2015.2499273
Grant ID: 2012CB316400
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