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Laplacian Regularization For Fuzzy Subspace Clustering

Abstract : This paper studies a well-established fuzzy subspace clustering paradigm and identifies a discontinuity in the produced solutions, which assigns neighbor points to different clusters and fails to identify the expected subspaces in these situations. To alleviate this drawback, a regularization term is proposed, inspired from clustering tasks for graphs such as spectral clustering. A new cost function is introduced, and a new algorithm based on an alternate optimization algorithm, called Weighted Laplacian Fuzzy Clustering, is proposed and experimentally studied.
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Contributor : Christophe Marsala <>
Submitted on : Monday, July 3, 2017 - 8:39:38 PM
Last modification on : Thursday, March 21, 2019 - 1:06:02 PM


  • HAL Id : hal-01555271, version 1


Arthur Guillon, Marie-Jeanne Lesot, Christophe Marsala. Laplacian Regularization For Fuzzy Subspace Clustering. IEEE International Conference on Fuzzy Systems (FuzzIEEE'17), Jul 2017, Napoli, Italy. ⟨hal-01555271⟩



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