Joint principal component and discriminant analysis for dimensionality reduction

Xiaowei Zhao, Jun Guo, Feiping Nie, Ling Chen, Zhihui Li, Huaxiang Zhang

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, 'most variance' is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.

Original languageEnglish
Article number8718522
Pages (from-to)433-444
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number2
DOIs
Publication statusPublished - 20 May 2020
Externally publishedYes

Keywords

  • Joint principal component and discriminant analysis (JPCDA)
  • small sample size problem
  • the most discriminant information
  • the null space of total scatter matrix

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