Information Preserving Discriminant Projections

Jing Peng, Alex J. Aved

2020

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.

Paper Citation

in Harvard Style

Peng J. and Aved A. (2020). Information Preserving Discriminant Projections. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 162-171. DOI: 10.5220/0008967601620171

in Bibtex Style

@conference{icaart20,
author={Jing Peng and Alex Aved},
title={Information Preserving Discriminant Projections},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={162-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008967601620171},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Information Preserving Discriminant Projections
SN - 978-989-758-395-7
AU - Peng J.
AU - Aved A.
PY - 2020
SP - 162
EP - 171
DO - 10.5220/0008967601620171