摘要
This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are much faster than those of binary principal component analysis (B-PCA). Besides, B-SNMF is more robust to occlusions in images. Experimental results on face images demonstrate the effectiveness and the efficiency of the proposed B-SNMF.
源语言 | 英语 |
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文章编号 | 4801604 |
页(从-至) | 772-777 |
页数 | 6 |
期刊 | IEEE Transactions on Circuits and Systems for Video Technology |
卷 | 19 |
期 | 5 |
DOI | |
出版状态 | 已出版 - 5月 2009 |
已对外发布 | 是 |