Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)

A Margin Technique for Dimension Reduction with Applications to Hyperspectral Imagery

Authors
Jing Peng, Kun Zhang
Corresponding Author
Jing Peng
Available Online August 2013.
DOI
https://doi.org/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.
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Proceedings
2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90-78677-74-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/icacsei.2013.132How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.132
DO  - https://doi.org/10.2991/icacsei.2013.132
ID  - Peng2013/08
ER  -