A Margin Technique for Dimension Reduction with Applications to Hyperspectral Imagery

DOI

10.2991/icacsei.2013.132How to use a DOI?

Keywords

Classification, dimensionality reduction, Relief

Abstract

Target classification in hyperspectral imagery has been demonstrated to be very useful in remote-sensing applications. While spectral bands provide information for classification, they give rise to a large number of features. However, a large number of features often degrade performance. In such situations, dimensionality reduction can be very helpful. There are many such techniques in the literature, and the most popular one is Fisher's linear discriminant analysis (LDA). For two class problems, LDA can be shown to be optimal. For the multi-class case, LDA is not. As such, a multi-class problem is cast into a binary one. This formulation not only simplifies the problem but also works well in practice. However, it lacks theoretical justification. We show in this paper the connection between the above formulation and Relief feature selection, thereby providing a sound basis for observed benefits associated with this formulation. Furthermore, we propose a margin based algorithm for dimensionality reduction that addresses some of the problems facing the two class formulation. We provide experimental results that corroborate well with our analysis.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jing Peng
AU  - Kun Zhang
PY  - 2013/08
DA  - 2013/08
TI  - A Margin Technique for Dimension Reduction with Applications to Hyperspectral Imagery
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 547
EP  - 551
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.132
DO  - 10.2991/icacsei.2013.132
ID  - Peng2013/08
ER  -