Abstract
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Multi-class image classification can benefit much from feature
subset selection. This paper extends an error bound of binary SVMs
to a feature subset selection criterion for the multi-class SVMs. By minimizing
this criterion, the scale factors assigned to each feature in a
kernel function are optimized to identify the important features. This
minimization problem can be efficiently solved by gradient-based search
techniques, even if hundreds of features are involved. Also, considering
that image classification is often a small sample problem, the regularization
issue is investigated for this criterion, showing its robustness in this
situation. Experimental study on multiple benchmark image data sets
demonstrates the effectiveness of the proposed approach.