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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shengcai Liao Zhen Lei Li, S.Z. |
| Copyright Year | 2009 |
| Description | Author affiliation: Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100190, China (Shengcai Liao; Zhen Lei; Li, S.Z.) |
| Abstract | In this paper, we present a Gibbs Random Field (GRF) modeling based Nonnegative Matrix Factorization (NMF) algorithm, called GRF-NMF. We propose to treat the component matrix of NMF as a Gibbs random field. Since each component presents a localized object part, as usually expected, we propose an energy function with the prior knowledge of smoothness and locality. This way of directly modeling on the structure of components makes the algorithm able to learn sparse, smooth, and localized object parts. Furthermore, we find that at each update iteration, the constrained term can be processed conveniently via local filtering on components. Finally we give a well established convergence proof for the derived algorithm. Experimental results on both synthesized and real image databases shows that the proposed GRF-NMF algorithm significantly outperforms other NMF related algorithms in sparsity, smoothness, and locality of the learned components. |
| Starting Page | 79 |
| Ending Page | 86 |
| File Size | 1556281 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424444427 |
| DOI | 10.1109/ICCVW.2009.5457714 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-27 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biometrics Bayesian methods Conferences Laboratories Clustering algorithms Matrix decomposition Sparse matrices National security Spectral analysis Convergence |
| Content Type | Text |
| Resource Type | Article |
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