Canonical correlation analysis based on local sparse representation and linear discriminative analysis

被引：0
|
作者：
机构：
[1] Department of Communication Countermeasure, Electronic Engineering Institute
[2] Key Laboratory of Electronic Restriction of Anhui Province, Electronic Engineering Institute
来源：
Xia, J.-M. (jianmingeei@163.com) | 1600年 / Northeast University卷 / 29期
关键词：
Canonical correlation analysis; Feature fusion; Linear discriminative analysis; Local sparse representation;
D O I：
10.13195/j.kzyjc.2013.0444
中图分类号：
学科分类号：
摘要：
The natural discriminating information contained in the data structure and class information of the datasets is very vital for the feature fusion. Then in order to utilize all the information, a canonical correlation analysis algorithm based on local sparse representation and linear discriminative analysis is proposed. Firstly, the local sparse representation method is utilized to obtain the sparse manifold reconstruction matrix with less computational complexity. Then, the united optimization is realized in the canonical correlation analysis scheme to constrain the sparse reconstructive relationship among each feature set with optimizing the combined discriminability and the feature correlation simultaneously, so that the discrimination capability of the feature extracted is increased. Finally, the simulation examples on artificial dataset, multiple feature database and facial databases are presented, and the experimental results show the effectiveness of the proposed method.
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页码：1279 / 1284
页数：5
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