Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension
This work addresses computational and accuracy challenges in hyperspectral image analysis for remote sensing or similar domains, but it is incremental as it applies an existing algorithm to this specific context.
The paper tackled the problem of selecting informative bands in hyperspectral data classification by minimizing a tight bound on the VC dimension, resulting in a method that outperformed state-of-the-art approaches in both sparsity and accuracy.
Hyperspectral data consists of large number of features which require sophisticated analysis to be extracted. A popular approach to reduce computational cost, facilitate information representation and accelerate knowledge discovery is to eliminate bands that do not improve the classification and analysis methods being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the specifics of the classification method being used. This paper employs a recently proposed filter-feature-selection algorithm based on minimizing a tight bound on the VC dimension. We have successfully applied this algorithm to determine a reasonable subset of bands without any user-defined stopping criteria on widely used hyperspectral images and demonstrate that this method outperforms state-of-the-art methods in terms of both sparsity of feature set as well as accuracy of classification.\end{abstract}