摘要
Non-negative matrix factorization (NMF) ignores both the local geometric structure of and the discriminative information contained in a data set. A manifold geometry-based NMF dimension reduction method called local discriminant NMF (LDNMF) is proposed in this paper. LDNMF preserves not only the non-negativity but also the local geometric structure and discriminative information of the data. The local geometric and discriminant structure of the data manifold can be characterized by a within-class graph and a between-class graph. An efficient multiplicative updating procedure is produced, and its global convergence is guaranteed theoretically. Experimental results on two hyperspectral image data sets show that the proposed LDNMF is a powerful and promising tool for extracting hyperspectral image features.
源语言 | 英语 |
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页(从-至) | 5073-5093 |
页数 | 21 |
期刊 | International Journal of Remote Sensing |
卷 | 35 |
期 | 13 |
DOI | |
出版状态 | 已出版 - 7月 2014 |