基于局部保留投影的堆叠隐空间模糊C均值算法*

传统模糊聚类算法在处理复杂非线性数据时学习能力较差。针对此问题，文中基于极限学习机(ELM)理论，结合局部保留投影(LPP)与ELM特征映射，提出压缩隐空间特征映射算法，从而将原始数据从原空间映射至压缩ELM隐空间中。通过连接多个压缩隐空间特征映射，结合模糊聚类技术，提出基于LPP的堆叠隐空间模糊C均值算法。大量实验表明，文中算法对模糊指数的变化不敏感，在处理复杂非线性数据和存在类内差异的图像数据时，能够取得更精确、高效、稳定的学习效果。

Cascaded Hidden Space Fuzzy C-means Based on Local Preserving Projection

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.