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Proximal Optimization for Fuzzy Subspace Clustering

Abstract : This paper proposes a fuzzy partitioning subspace clustering algorithm that minimizes a variant of the FCM cost function with a weighted Euclidean distance and a penalty term. To this aim it considers the framework of proximal optimization. It establishes the expression of the proximal operator for the considered cost function and derives PFSCM, an algorithm combining proximal descent and alternate optimization. Experiments show the relevance of the proposed approach.
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https://hal.sorbonne-universite.fr/hal-01364652
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Submitted on : Monday, September 12, 2016 - 5:25:56 PM
Last modification on : Tuesday, January 12, 2021 - 9:52:02 AM
Long-term archiving on: : Tuesday, December 13, 2016 - 4:03:38 PM

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Arthur Guillon, Marie-Jeanne Lesot, Christophe Marsala, Nikhil R. Pal. Proximal Optimization for Fuzzy Subspace Clustering. 16th International Conference on Information processing and Management of Uncertainty (IPMU 2016), Jun 2016, Eindhoven, Netherlands. pp.675-686, ⟨10.1007/978-3-319-40596-4_56⟩. ⟨hal-01364652⟩

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