Sparse subspace clustering via group sparse coding

Saha, Budhaditya, Pham, Duc Son, Phung, Dinh and Venkatesh, Svetha 2013, Sparse subspace clustering via group sparse coding, in SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Austin, Texas, pp. 130-138.

Attached Files
Name Description MIMEType Size Downloads

Title Sparse subspace clustering via group sparse coding
Author(s) Saha, Budhaditya
Pham, Duc Son
Phung, Dinh
Venkatesh, Svetha
Conference name International Conference on Data Mining (13th : 2013 : Austin, Texas)
Conference location Austin, Texas
Conference dates 2-4 May 2013
Title of proceedings SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2013
Conference series International Conference on Data Mining
Start page 130
End page 138
Total pages 9
Publisher Society for Industrial and Applied Mathematics
Place of publication Austin, Texas
Summary 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.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30053589

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 37 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 12 Jul 2013, 15:22:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.