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 language | English (US) |
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Pages (from-to) | 629-634 |
Number of pages | 6 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
State | Published - 2007 |
Event | 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India Duration: Jan 6 2007 → Jan 12 2007 |
ASJC Scopus subject areas
- Artificial Intelligence