[1]ZHU Huanrong,ZHENG Zhichao,SUN Huaijiang.Locality-regularized linear regression classification-based discriminant analysis[J].CAAI Transactions on Intelligent Systems,2019,14(5):959-965.[doi:10.11992/tis.201808007]
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Locality-regularized linear regression classification-based discriminant analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 959-965 Column: 学术论文—机器学习 Public date: 2019-09-05
Title:
Locality-regularized linear regression classification-based discriminant analysis
Author(s):
ZHU Huanrong;  ZHENG Zhichao;  SUN Huaijiang
College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
Keywords:
locality-regularized linear regression classification; dimensionality reduction; orthogonal projections; trace ratio problem; face recognition; feature extraction; discriminant analysis; linear regression classification
CLC:
TP391
DOI:
10.11992/tis.201808007
Abstract:
Locality-regularized linear regression classification (LLRC) based face recognition achieves high accuracy and high efficiency. However, the original feature space cannot guarantee the efficiency of LLRC. To improve the performance of LLRC, this study proposes a new dimensionality reduction method called locality-regularized linear regression classification-based discriminant analysis (LLRC-DA), which is directly associated with LLRC. The objective function of LLRC-DA is designed according to the classification rule of LLRC. In LLRC, interclass local reconstruction errors are maximized and simultaneously, intraclass local reconstruction errors are minimized to identify the optimal feature subspace. In addition, LLRC-DA eliminates redundant information using an orthogonal constraint, imposed on the projection matrix. To effectively obtain the solutions of the projection matrix, we exploit the relationship between optimal variables and propose a new trace ratio optimization method. Hence, LLRC-DA suits LLRC well. Extensive experimental results obtained from the FERET and ORL face databases demonstrate the superiority of the proposed method than state-of-the-art methods.
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