期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 35, 期 13, 页码 5073-5093出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2014.930198
关键词
-
资金
- National Natural Science Foundation of China [61301196, 61371152, 61071172, 61201323, 11202161]
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.
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