Low-Rank Approximation and Decomposition for Kernel Matrix Based on Column Correlation
LIU Song-Hua1, ZHANG Jun-Ying1 , DING Cai-Ying2,3
1.School of Computer Science and Engineering, Xidian University, Xi’an 710071 2.Center of Interdisciplinary Studies, Lanzhou University, Lanzhou 730000 3.Laboratory of Condensed Matter Theory and Materials Computation, Institute of Physics, Chinese Academy of Sciences, Beijing 100170
Abstract An effective method of low-rank approximation and decomposition for kernel matrix is proposed . Firstly, aiming at the assumption that column of the kernel matrix is independent from its class label, the correlation of columns is studied and a strategy for column selection is designed. Secondly, the kernel matrix is decomposed into two stages: low-rank matrix decomposition and extension. Then an expectation of low-rank approximation error bound is given. The proposed algorithm extracts discriminative sub-matrix without independent assumption. In this way, it avoids the decomposition of the entire kernel matrix and effectively reduces the computational complexity. Finally, the experimental results show that the proposed method is effective and reasonable.
LIU Song-Hua,ZHANG Jun-Ying,DING Cai-Ying. Low-Rank Approximation and Decomposition for Kernel Matrix Based on Column Correlation[J]. , 2011, 24(6): 776-782.
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2011, Vol. 24 
