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) |
|---|---|
| Pages (from-to) | 629-634 |
| Number of pages | 6 |
| Journal | IJCAI International Joint Conference on Artificial Intelligence |
| State | Published - Dec 1 2007 |
| Event | 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India Duration: Jan 6 2007 → Jan 12 2007 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
A convergent solution to tensor subspace learning. / Wang, Huan; Yan, Shuicheng; Huang, Thomas S; Tang, Xiaoou.
In: IJCAI International Joint Conference on Artificial Intelligence, 01.12.2007, p. 629-634.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - A convergent solution to tensor subspace learning
AU - Wang, Huan
AU - Yan, Shuicheng
AU - Huang, Thomas S
AU - Tang, Xiaoou
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84880903188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880903188&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84880903188
SP - 629
EP - 634
JO - IJCAI International Joint Conference on Artificial Intelligence
JF - IJCAI International Joint Conference on Artificial Intelligence
SN - 1045-0823
ER -