A Bayes Optimal Framework for Pattern Classification

We present a Bayes optimal framework to improve existing pattern classification methods. The idea is that we first derive a new space of representations where the Bayes error is potentially to be smaller, and then any classification approaches can be directly employed in this space to obtain higher classification accuracies. Extensive experiments with well-known classification approaches demonstrate the effectiveness of our framework in improving the classification performance.