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.
刘欢，王骏， 应文豪，王士同. 基于局部保留投影的堆叠隐空间模糊C均值算法*[J]. 模式识别与人工智能, 2016, 29(9): 807-815.
LIU Huan, WANG Jun, YING Wenhao, WANG Shitong. Cascaded Hidden Space Fuzzy C-means Based on Local Preserving Projection. , 2016, 29(9): 807-815.
[1] 朴尚哲,超木日力格,于 剑.模糊C均值算法的聚类有效性评价.模式识别与人工智能, 2015, 28(5): 452-461. (PIAO S Z, CHAOMURILIGE, YU J. Cluster Validity Indexes for FCM Clustering Algorithm. Pattern Recognition and Artificial Intelligence, 2015, 28(5): 452-461.) [2] 王 骏,王士同,邓赵红.聚类分析研究中的若干问题.控制与决策, 2012, 27(3): 321-328. (WANG J, WANG S T, DENG Z H. Survey on Challenges in Clu-stering Analysis Research. Control and Decision, 2012, 27(3): 321-328.) [3] 蒋亦樟,邓赵红,王 骏,等.熵加权多视角协同划分模糊聚类算法.软件学报, 2014, 25(10): 2293-2311. (JIANG Y Z, DENG Z H, WANG J, et al. Collaborative Partition Multi-view Fuzzy Clustering Algorithm Using Entropy Weighting. Journal of Software, 2014, 25(10): 2293-2311.) [4] N'CIR C E B, ESSOUSSI N, LIMAM M. Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries. Journal of Classification, 2015, 32(2): 176-211. [5] GRAVES D, PEDRYCZ W. Kernel-Based Fuzzy Clustering and Fuzzy Clustering: A Comparative Experimental Study. Fuzzy Sets and Systems, 2010, 161(4): 522-543. [6] FIGUERA C, BARQUERO-PREZ , ROJO-LVAREZ J L, et al. Spectrally Adapted Mercer Kernels for Support Vector Nonuniform Interpolation. Signal Processing, 2014, 94: 421-433. [7] WANG Y G, CAO F L, YUAN Y B. A Study on Effectiveness of Extreme Learning Machine. Neurocomputing, 2011, 74(16): 2483-2490. [8] DING S F, ZHAO H, ZHANG Y N, et al. Extreme Learning Machine: Algorithm, Theory and Applications. Artificial Intelligence Review, 2015, 44(1): 103-115. [9] HUANG G B, WANG D H, LAN Y. Extreme Learning Machines: A Survey. International Journal of Machine Learning and Cyberne-tics, 2011, 2(2): 107-122. [10] HE Q, JIN X, DU C Y, et al. Clustering in Extreme Learning Machine Feature Space. Neurocomputing, 2014, 128: 88-95. [11] BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms. Berlin, Germany: Springer, 1981. [12] AHMED N. Recent Review on Image Clustering. IET Image Processing, 2015, 9(11): 1020-1032. [13] SHIKKENAWIS G, MITRA S K. On Some Variants of Locality Preserving Projection. Neurocomputing, 2016, 173: 196-211. [14] 申中华,潘永惠,王士同.有监督的局部保留投影降维算法.模式识别与人工智能, 2008, 21(2): 233-239. (SHEN Z H, PAN Y H, WANG S T. A Supervised Locality Preserving Projection Algorithm for Dimensionality Reduction. Pattern Recognition and Artificial Intelligence, 2008, 21(2): 233-239.) [15] 于 剑.论模糊C均值算法的模糊指标.计算机学报, 2003, 26(8): 968-973. (YU J. On the Fuzziness Index of the FCM Algorithms. Chinese Journal of Computers, 2003, 26(8): 968-973.) [16] SABO K, SCITOVSKI R. Interpretation and Optimization of the K-means Algorithm. Applications of Mathematics, 2014, 59(4): 391-406. [17] GAN G, WU J. A Convergence Theorem for the Fuzzy Subspace Clustering (FSC) Algorithm. Pattern Recognition, 2008, 41(6):1939-1947. [18] SAMARIA F S, HARTER A C. Parameterisation of a Stochastic Model for Human Face Identification // Proc of the 2nd IEEE Workshop on Applications of Computer Vision. New York, USA: IEEE, 1994: 138-142. [19] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfa- ces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720. [20] HULL J J. A Database for Handwritten Text Recognition Research. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(5): 550-554. [21] BRO R, SMILDE A K. Principal Component Analysis. Analytical Methods, 2014, 6: 2812-2831. [22] IMANI M, GHASSEMIAN H. Two Dimensional Linear Discriminant Analyses for Hyperspectral Data. Photogrammetric Enginee-ring & Remote Sensing, 2015, 81(10): 777-786.
2016, Vol. 29 
