Information preserving discriminant projections

Jing Peng, Alex J. Aved

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In classification, a large number of features often make the design of a classifier difficult and degrade its performance. This is particularly pronounced when the number of examples is small relative to the number of features, which is due to the curse of dimensionality. There are many dimensionality reduction techniques in the literature. However, most these techniques are either informative (or minimum information loss), as in principal component analysis (PCA), or discriminant, as in linear discriminant analysis (LDA). Each type of technique has its strengths and weaknesses. Motivated by Gaussian Processes Latent Variable Models, we propose a simple linear projection technique that explores the characteristics of both PCA and LDA in latent representations. The proposed technique optimizes a regularized information preserving objective, where the regularizer is a LDA based criterion. And as such, it prefers a latent space that is both informative and discriminant, thereby providing better generalization performance. Experimental results based on a variety of data sets are provided to validate the proposed technique.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages162-171
Number of pages10
ISBN (Electronic)9789897583957
StatePublished - 1 Jan 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
CountryMalta
CityValletta
Period22/02/2024/02/20

Keywords

  • Classification
  • Dimensionality Reduction
  • Feature Selection

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  • Cite this

    Peng, J., & Aved, A. J. (2020). Information preserving discriminant projections. In A. Rocha, L. Steels, & J. van den Herik (Eds.), ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence (pp. 162-171). (ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence; Vol. 2). SciTePress.