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| 堆叠隐空间模糊C 均值聚类算法 | |
| 引用本文： | 王骏,刘欢,蒋亦樟,邓赵红,王士同.堆叠隐空间模糊C 均值聚类算法[J].控制与决策,2016,31(9):1671-1677. |
| 作者姓名： | 王骏 刘欢 蒋亦樟 邓赵红 王士同 |
| 作者单位： | 江南大学数字媒体学院，江苏无锡214122. |
| 基金项目： | 国家自然科学基金项目(61300151)；江苏省自然科学基金项目(BK20130155)；江苏省高校自然科学研究项目(13KJB520001). |
| 摘 要： | 基于极限学习机理论, 将主成分分析技术与ELM特征映射相结合, 提出一种基于主成分分析的压缩隐空间构建新方法. 结合多层神经网络学习方法对隐空间进行多层融合, 进一步提出了堆叠隐空间模糊C 均值聚类算法,从而提高对非线性数据的学习能力. 实验结果表明, 所提出算法在处理复杂非线性数据时更加高效、稳定, 同时克服了模糊聚类算法对模糊指数的敏感性问题. |
| 关 键 词： | 隐空间映射|极限学习机|主成分分析|模糊C 均值聚类|多层神经网络 |
| 收稿时间： | 2015/6/16 0:00:00 |
| 修稿时间： | 2016/1/18 0:00:00 |
Cascaded hidden space fuzzy C means clustering algorithm |
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| WANG Jun LIU Huan JIANG Yi-zhang DENG Zhao-hong WANG Shi-tong.Cascaded hidden space fuzzy C means clustering algorithm[J].Control and Decision,2016,31(9):1671-1677. | |
| Authors: | WANG Jun LIU Huan JIANG Yi-zhang DENG Zhao-hong WANG Shi-tong |
| Abstract: | In view of the good properties of the extreme learning machine(ELM) feature mapping, a novel technique of constructing to condensed hidden feature space is proposed by combining principal component analysis(PCA) with ELM feature mapping. The cascaded hidden space fuzzy C means clustering algorithm is proposed to improve the learning ability of the non-linear data. Experimental results show that the proposed algorithm is not only efficient and robust for high-dimension data, but also insensitive to the fuzzy index of fuzzy clustering algorithms. |
| Keywords: | hidden-mapping space|extreme learning machine|principal component analysis|fuzzy C means clustering|multi-layer neural network |
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