A Margin Technique for Dimension Reduction with Applications to Hyperspectral Imagery
Jing Peng, Kun Zhang
Available Online August 2013.
- https://doi.org/10.2991/icacsei.2013.132How to use a DOI?
- Classification, dimensionality reduction, Relief
- 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.
- 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 -