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Compound rank-k projections for bilinear analysis

journal contribution
posted on 2024-11-02, 17:57 authored by Xiaojun ChangXiaojun Chang, Feiping Nie, Sen Wang, Yi Yang, Xiaofang Zhou, Chengqi Zhang
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.

Funding

Predicting health status of geriatric patients from user trusted multimedia observations

Australian Research Council

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History

Journal

IEEE Transactions on Neural Networks and Learning Systems

Volume

27

Number

7161356

Issue

7

Start page

1502

End page

1513

Total pages

12

Publisher

IEEE

Place published

United States

Language

English

Copyright

© 2015 IEEE.

Former Identifier

2006109457

Esploro creation date

2021-08-29

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