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Sparsity-Inducing Fuzzy Subspace Clustering

Guillon, Arthur; Lesot, Marie-Jeanne; Marsala, Christophe


This paper considers a fuzzy subspace clustering problem and proposes to introduce an original sparsity-inducing regularization term. The minimization of this term, which involves a l$_{0}$ penalty, is considered from a geometric point of view and a novel proximal operator is derived. A subspace clustering algorithm, Prosecco, is proposed to optimize the cost function using both proximal and alternate gradient descent. Experiments comparing this algorithm to the state of the art in sparse fuzzy subspace clustering show the relevance of the proposed approach.

Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/08
Veröffentlicht am 01.10.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000138413
Erschienen in Archives of Data Science, Series A
Band 5
Heft 1
Seiten P08, 22 S. online
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