Cascaded Hidden Space Fuzzy C-means Based on Local Preserving Projection
LIU Huan1, WANG Jun1, YING Wenhao2, WANG Shitong1
1.School of Digital Media, Jiangnan University, Wuxi 214122.2.School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500
Abstract The traditional fuzzy clustering algorithms have poor learning ability for complex nonlinear data. Aiming at this problem, a condensed hidden space feature mapping is proposed by combining local preserving projection (LPP) and extreme learning machine (ELM) feature mapping. Thus, the original data is mapped into the condensed ELM hidden space. By connecting several condensed hidden space feature mapping together and combining fuzzy clustering methods, the cascaded ELM hidden space is constructed and a cascaded hidden space fuzzy clustering algorithm is proposed. Experimental results show that the proposed algorithm is insensitive to fuzzy index and efficient and robust for non-linear data and image data with intra-class variation.
About author:: LIU Huan(Corresponding author),born in 1993,master student. His research interests include intelligent computation and data mining.WANG Jun, born in 1978, Ph.D.candidate, associate professor. His research interests include intelligent computation and data mining.YING Wenhao, born in 1979, Ph.D.candidate, associate professor. His research interests include pattern recognition and intelligent computation.WANG Shitong, born in 1964, Ph.D., professor. His research interests include pattern recognition and artificial intelligence.
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2016, Vol. 29 
