Local discriminant non-negative matrix factorization feature extraction for hyperspectral image classification

J. H. Wen, Y. Q. Zhao, X. F. Zhang, W. D. Yan, W. Lin

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12 引用 (Scopus)

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
页(从-至)5073-5093
页数21
期刊International Journal of Remote Sensing
35
13
DOI
出版状态已出版 - 7月 2014

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