Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA, DATER and Tensor Subspace Analysis (TSA). 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.

Authors

• Huan Wang
• Shuicheng Yan Skywork AI, Singapore Nanyang Technological University, Singapore
• Thomas Huang
• Xiaoou Tang

Keywords

No keywords are indexed for this paper.