[1]LI Jing,CHEN Xiuhong.Competitive collaborative representation-based local discriminant projection for feature extraction[J].CAAI Transactions on Intelligent Systems,2019,14(5):974-981.[doi:10.11992/tis.201809020]
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Competitive collaborative representation-based local discriminant projection for feature extraction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 974-981 Column: 学术论文—人工智能基础 Public date: 2019-09-05
Title:
Competitive collaborative representation-based local discriminant projection for feature extraction
Author(s):
LI Jing;  CHEN Xiuhong
School of Digital Media, Jiangnan University, Wuxi 214000, China
Keywords:
feature extraction; collaborative representation; pattern recognition; positive coefficient; competitive; robustness; local structure; face image
CLC:
TP391.4
DOI:
10.11992/tis.201809020
Abstract:
The feature extraction algorithm uses the cooperative representation relation between samples to construct the adjacency graph, which only considers the synergy of all training samples and ignores the competitiveness of each type of training sample. Therefore, based on competitive cooperative representation, this study proposes a local discriminant projection feature extraction algorithm and further constructs between-class and within-class graphs. Considering the influence of each type of coefficient in the adjacency graph, we introduce the idea of retaining the positive representation coefficient in the sparse adjacency graph. The local structure of the image is characterized by calculating the within-class and between-class scatter matrices; furthermore, the optimal projection matrix is obtained. The experimental results of some data sets show that compared with similar feature extraction algorithms based on local discriminant projection, the algorithm exhibits good recognition effect and good robustness in noise and occlusion and effectively increases the image recognition efficiency.
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