Proximal Optimization for Fuzzy Subspace Clustering

Arthur Guillon 1 Marie-Jeanne Lesot 1 Christophe Marsala 1 Nikhil Pal 2
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
2 Indian Statistical Institute [Kolkata]
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.
Keywords : proximal descent Fuzzy partitioning clustering subspace clustering
Type de document :
Communication dans un congrès
16th International Conference on Information processing and Management of Uncertainty (IPMU 2016), Jun 2016, Eindhoven, Netherlands. Information Processing and Management of Uncertainty in Knowledge-Based Systems 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part I, 610, pp.675-686, 2016, Communications in Computer and Information Science. 〈10.1007/978-3-319-40596-4_56〉
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Arthur Guillon, Marie-Jeanne Lesot, Christophe Marsala, Nikhil Pal. Proximal Optimization for Fuzzy Subspace Clustering. 16th International Conference on Information processing and Management of Uncertainty (IPMU 2016), Jun 2016, Eindhoven, Netherlands. Information Processing and Management of Uncertainty in Knowledge-Based Systems 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part I, 610, pp.675-686, 2016, Communications in Computer and Information Science. 〈10.1007/978-3-319-40596-4_56〉. 〈hal-01364652〉

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