We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.
History
Event
International Conference on Data Mining (13th : 2013 : Austin, Texas)
Pagination
130 - 138
Publisher
Society for Industrial and Applied Mathematics
Location
Austin, Texas
Place of publication
Austin, Texas
Start date
2013-05-02
End date
2013-05-04
Language
eng
Publication classification
E1 Full written paper - refereed; E Conference publication
Title of proceedings
SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining