Linear manifold clustering for high dimensional data based on line manifold searching and fusing |
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Authors: | Gang-guo Li Zheng-zhi Wang Xiao-min Wang Qing-shan Ni and Bo Qiang |
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Affiliation: | (1) Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Meselik, 26480 Eskisehir, Turkey;(2) Learning and Recognition in Vision (LEAR), INRIA, Grenoble, France;(3) Department of Mathematics, Vanderbilt University, Nashville, TN, USA;(4) Laboratoire Jean Kuntzmann, Grenoble, France;(5) University of Caen, Caen, France |
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Abstract: | High dimensional data clustering, with the inherent sparsity of data and the existence of noise, is a serious challenge for
clustering algorithms. A new linear manifold clustering method was proposed to address this problem. The basic idea was to
search the line manifold clusters hidden in datasets, and then fuse some of the line manifold clusters to construct higher
dimensional manifold clusters. The orthogonal distance and the tangent distance were considered together as the linear manifold
distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line
manifolds during the line manifold cluster searching procedure. The results obtained from experiments over real and synthetic
data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and
computation time. The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,
manifold dimensions and noise ratios, which confirms the anti-noise capability and high clustering accuracy of the proposed
method for high dimensional data. |
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Keywords: | linear manifold subspace clustering line manifold data mining data fusing clustering algorithm |
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