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Soft Computing

, Volume 23, Issue 14, pp 5511–5518 | Cite as

Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction

  • Minghua WanEmail author
  • Guowei Yang
  • Chengli Sun
  • Maoxi Liu
Methodologies and Application
  • 99 Downloads

Abstract

Two-dimensional locality-preserving projection (2DLPP) is an unsupervised method, so it can’t use the discrimination information of the sample in the sparse data; elastic net regression can obtain a sparse results of the feature extraction. So, this paper presents a new method for image feature extraction, namely the sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) based on the 2D discriminant locality-preserving projection (2DDLPP) and elastic net regression. By adding the between-class scatter and discrimination information into the objective function of 2DLPP, S2DDLPP uses elastic net regression to obtain an optimal sparse projection matrix with “minimizing the within-class scatter” and “maximizing the between-class scatter.” Compared with other methods (2DPCA, 2DPCA-L1, 2DLDA, 2DLPP, 2DDLPP, and 2DDLPP-L1), the experimental results on the ORL, Yale, AR and FERET face database show the effectiveness of the proposed algorithm.

Keywords

Two-dimensional locality preserving projection Elastic net regression Sparsity Feature extraction Discrimination information 

Notes

Acknowledgements

This work is partially supported by National Key R&D Program Grant No. 2017YFC0804002, the National Science Foundation of China under Grant Nos. 61462064, 6177227, 61362031, 61463008, 61403188, 61503195, 61603192, and the China Postdoctoral Science Foundation under Grant No. 2016M600674, the Natural Science Fund of Jiangsu Province under Grants BK20161580, BK20171494 and China’s Aviation Science (No. 20145556011).

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Minghua Wan
    • 1
    • 2
    • 3
    Email author
  • Guowei Yang
    • 1
  • Chengli Sun
    • 4
  • Maoxi Liu
    • 4
  1. 1.School of Information EngineeringNanjing Audit UniversityNanjingPeople’s Republic of China
  2. 2.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China
  3. 3.Key Laboratory of Trusted Cloud Computing and Big Data AnalysisNanjing Xiaozhuang UniversityNanjingPeople’s Republic of China
  4. 4.Key Laboratory of Jiangxi Province for Image Processing and Pattern RecognitionNanchang Hangkong UniversityNanchangPeople’s Republic of China

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