基于局部保留投影的堆叠隐空间模糊<i>C</i>均值算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201609005

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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.

Key words： Hidden-Mapping Space Extreme Learning Machine (ELM) Local Preserving Projection(LPP) Fuzzy C-means Clustering Image Clustering

Received: 07 January 2016

ZTFLH:

TP 181

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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	LIU Huan
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	WANG Shitong

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LIU Huan,WANG Jun,YING Wenhao等. Cascaded Hidden Space Fuzzy C-means Based on Local Preserving Projection[J]. , 2016, 29(9): 807-815.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201609005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I9/807

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