Selecting Shape Features Using Multi-class Relevance Vector Machine

The task of visual object recognition benefits from feature selection as it reduces the amount of computation in recognizing a new instance of an object, and the selected features give insights into the classification process. We focus on a class of current feature selection methods known as embedded methods: due to the nature of multi-way classification in object recognition, we derive an extension of the Relevance Vector Machine technique to multi-class. In experiments, we apply Relevance Vector Machine on the problem of digit classification and study its effects. Experimental results show that our classifier enhances accuracy, yields good interpretation for the selected subset of features and costs only a constant factor of the baseline classifier.