Scalable spectral k-support norm regularization for robust low rank subspace learning

Yiu Ming CHEUNG, Jian Lou

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

As a fundamental tool in the fields of data mining and computer vision, robust low rank subspace learning is to recover a low rank matrix under gross corruptions that are often modeled by another sparse matrix. Within this learning, we investigate the spectral k-support norm, a more appealing convex relaxation than the popular nuclear norm, as a low rank penalty in this paper. Despite the better recovering performance, the spectral k-support norm entails the model difficult to be optimized efficiently, which severely limits its scalability from the practical perspective. Therefore, this paper proposes a scalable and efficient algorithm which considers the dual objective of the original problem that can take advantage of the more computational efficient linear oracle of the spectral k-support norm to be evaluated. Further, by studying the sub-gradient of the loss of the dual objective, a line-search strategy is adopted in the algorithm to enable it to adapt to the Holder smoothness. Experiments on various tasks demonstrate the superior prediction performance and computation efficiency of the proposed algorithm.

Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1151-1160
Number of pages10
ISBN (Electronic)9781450340731
DOIs
Publication statusPublished - 24 Oct 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period24/10/1628/10/16

Scopus Subject Areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

User-Defined Keywords

  • Conditional gradient
  • Robust low rank subspace learning
  • Spectral k-support norm

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