Compound rank-k projections for bilinear analysis

Chang, Xiaojun, Nie, Feiping, Wang, Sen, Yang, Yi, Zhou, Xiaofang and Zhang, Chengqi (2016) Compound rank-k projections for bilinear analysis. IEEE Transactions on Neural Networks and Learning Systems, 27 7: 1502-1513. doi:10.1109/TNNLS.2015.2441735

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Author Chang, Xiaojun
Nie, Feiping
Wang, Sen
Yang, Yi
Zhou, Xiaofang
Zhang, Chengqi
Title Compound rank-k projections for bilinear analysis
Formatted title
Compound rank-k projections for bilinear analysis
Journal name IEEE Transactions on Neural Networks and Learning Systems   Check publisher's open access policy
ISSN 2162-2388
Publication date 2016-07-01
Year available 2016
Sub-type Article (original research)
DOI 10.1109/TNNLS.2015.2441735
Open Access Status Not yet assessed
Volume 27
Issue 7
Start page 1502
End page 1513
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1712 Software
1706 Computer Science Applications
1705 Computer Networks and Communications
1702 Artificial Intelligence
Abstract In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing 2-D discriminant analysis algorithms employ a single projection model to exploit the discriminant information for projection, making the model less flexible. In this paper, we propose a novel compound rank- k projection (CRP) algorithm for bilinear analysis. The CRP deals with matrices directly without transforming them into vectors, and it, therefore, preserves the correlations within the matrix and decreases the computation complexity. Different from the existing 2-D discriminant analysis algorithms, objective function values of CRP increase monotonically. In addition, the CRP utilizes multiple rank- k projection models to enable a larger search space in which the optimal solution can be found. In this way, the discriminant ability is enhanced. We have tested our approach on five data sets, including UUIm, CVL, Pointing'04, USPS, and Coil20. Experimental results show that the performance of our proposed CRP performs better than other algorithms in terms of classification accuracy.
Keyword Discriminant analysis
Feature extraction
High-order representation
Rank-k projection
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 61303143
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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