Laplacian Regularization For Fuzzy Subspace Clustering

Arthur Guillon 1 Marie-Jeanne Lesot 1 Christophe Marsala 1
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
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.
Type de document :
Communication dans un congrès
IEEE International Conference on Fuzzy Systems (FuzzIEEE'17), Jul 2017, Napoli, Italy. 2017
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https://hal.sorbonne-universite.fr/hal-01555271
Contributeur : Christophe Marsala <>
Soumis le : lundi 3 juillet 2017 - 20:39:38
Dernière modification le : jeudi 3 mai 2018 - 16:51:47

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  • HAL Id : hal-01555271, version 1

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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. 2017. 〈hal-01555271〉

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