Sparse Subspace Clustering via Group Sparse Coding

Budhaditya, S.; Pham, DucSon; Phung, D.; Venkatesh, S.

doi:10.1137/1.9781611972832.15

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Access Status

Open access

Authors

Budhaditya, S.

Pham, DucSon

Phung, D.

Venkatesh, S.

Date

2013

Type

Conference Paper

Metadata

Show full item record

Citation

Budhaditya, Saha and Pham, Duc Son and Phung, Dinh and Venkatesh, Svetha. 2013. Sparse Subspace Clustering via Group Sparse Coding, in Ghosh, J., Obradovic, Z., Dy, J., Zhoou, Z., Kamath, C., Parthasarathy, S. (ed), Proceedings of the 2013 SIAM International Conference on Data Mining, May 2-4 2013, pp. 130-138. Austin, Texas, USA: SIAM.

Source Title

Proceedings of the SIAM International Conference on Data Mining (SDM)

Source Conference

SIAM International Conference on Data Mining (SDM)

DOI

10.1137/1.9781611972832.15

ISBN

9781611972627

Remarks

NOTICE: This is the author’s version of a work in which changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

URI

http://hdl.handle.net/20.500.11937/16995

Collection

Curtin Research Publications

Abstract

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 efificient 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 outperform rival methods.