Multiple kernel concept factorization algorithm based on global fusion
LI Fei1,2, DU Liang1,2,3, REN Chaohong1,2
1. School of Computer and Information Technology, Shanxi University, Taiyuan Shanxi 030006, China; 2. Institute of Big Data Science and Industry, Shanxi University, Taiyuan Shanxi 030006, China; 3. Key Laboratory of Computational Intelligence and Chinese Information Processing, Ministry of Education(Shanxi University), Taiyuan Shanxi 030006, China
Abstract：Non-negative Matrix Factorization (NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization (CF) algorithm extends matrix factorization to single non-linear kernel space, improving learning ability and adaptability of matrix factorization. In unsupervised environment, to design or select proper kernel function for specific dataset, a new algorithm called Globalized Multiple Kernel CF (GMKCF) was proposed. Multiple candidate kernel functions were input in the same time and learned in the CF framework based on global linear fusion, obtaining a clustering result with high quality and stability and solving the problem of kernel function selection that the CF faced. The convergence of the proposed algorithm was verified by solving the model with alternate iteration. The experimental results on several real databases show that the proposed algorithm outperforms comparison algorithms in data clustering, such as Kernel K-Means (KKM), Spectral Clustering (SC), Kernel CF (KCF), Co-regularized multi-view spectral clustering (Coreg), and Robust Multiple KKM (RMKKM).
李飞, 杜亮, 任超宏. 基于全局融合的多核概念分解算法[J]. 计算机应用, 2019, 39(4): 1021-1026.
LI Fei, DU Liang, REN Chaohong. Multiple kernel concept factorization algorithm based on global fusion. Journal of Computer Applications, 2019, 39(4): 1021-1026.
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2019, Vol. 39 
