Initialization enhancer for non-negative matrix factorization |
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| Affiliation: | 1. Institute of Information Science and Engineering, P.O. box 143, Zhejiang Normal University, Jinhua 321004, Zhejiang, People''s Republic of China;2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, People''s Republic of China;3. Alexandria Research Institute, Virginia Polytechnic Institute and State University, 206 N. Washington Street, Alexandria, VA 22314, USA;1. School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi 537000, China;2. School of Electronics and Communication Engineering, Yulin Normal University, Yulin, Guangxi 537000, China;3. College of Chemistry and Food Science, Yulin Normal University, Yulin, Guangxi 537000, China;1. School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471003, People''s Republic of China;2. Department of Mathematics and Physics, Luoyang Institute of Science and Technology, Luoyang,471003, People''s Republic of China;1. School of Information Management, Central China Normal University, Wuhan 430079, China;2. School of Computer, Central China Normal University, Wuhan 430079, China;1. College of Science, China Agricultural University, Beijing 100083, China;2. Capital Normal University, Beijing 100048, China;3. College of Biotechnology, China Agricultural University, Beijing 100193, China;4. Imperial College London, London SW7 2AZ, United Kingdom |
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| Abstract: | Non-negative matrix factorization (NMF), proposed recently by Lee and Seung, has been applied to many areas such as dimensionality reduction, image classification image compression, and so on. Based on traditional NMF, researchers have put forward several new algorithms to improve its performance. However, particular emphasis has to be placed on the initialization of NMF because of its local convergence, although it is usually ignored in many documents. In this paper, we explore three initialization methods based on principal component analysis (PCA), fuzzy clustering and Gabor wavelets either for the consideration of computational complexity or the preservation of structure. In addition, the three methods develop an efficient way of selecting the rank of the NMF in low-dimensional space. |
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