A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features

In this paper, we present a new approach to the design of probabilistic classifiers. Rather than working with a common high-dimensional feature vector, the classifier is written in terms of separate feature vectors chosen specifically for each class and their low-dimensional PDFs. While sufficiency is not a requirement, if the feature vectors are sufficient to distinguish the corresponding class from a common (null) hypothesis, the method is equivalent to the maximum a posteriori probability (MAP) classifier. The method has applications to speech, image, and general pattern recognition problems.