A Convengent Solution to Tensor Subspace Learning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 629-633.
Abstract
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.
@inproceedings{ijcai2007p100,
title = {A Convengent Solution to Tensor Subspace Learning},
author = {Wang, Huan and Yan, Shuicheng and Huang, Thomas S. and Tang, Xiaoou},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {629--633},
year = {2007},
url = {https://ijcai.org/proceedings/2007/100},
}
TY - CPAPER TI - A Convengent Solution to Tensor Subspace Learning AU - Wang, Huan AU - Yan, Shuicheng AU - Huang, Thomas S. AU - Tang, Xiaoou T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 629 EP - 633 UR - https://ijcai.org/proceedings/2007/100 ER -Copied
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