A convergent solution to tensor subspace learning

Huan Wang, Shuicheng Yan, Thomas Huang, Xiaoou Tang

Research output: Contribution to journalConference articlepeer-review

Abstract

Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA [Ye et al., 2004], DATER [Yan et al., 2005] and Tensor Subspace Analysis (TSA) [He et al., 2005]. In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional solution procedures.

Original languageEnglish (US)
Pages (from-to)629-634
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

ASJC Scopus subject areas

  • Artificial Intelligence

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