Laplacian Regularization For Fuzzy Subspace Clustering - Sorbonne Université Access content directly
Conference Papers Year : 2017

Laplacian Regularization For Fuzzy Subspace Clustering


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.
No file

Dates and versions

hal-01555271 , version 1 (03-07-2017)


  • 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⟩
125 View
0 Download


Gmail Mastodon Facebook X LinkedIn More