Discriminative learning and informative learning in pattern recognition

Abstract

In pattern recognition, the goal of classification can be achieved from two different types of learning strategy-discriminative teaming and informative learning. Discriminative learning focuses on extracting the discriminative information between classes. Informative learning emphasizes the learning of the class information such as class densities. We review major discriminative learning methods, namely, principal component analysis (PCA), linear discriminant analysis (LDA), minimum classification error (MCE) training algorithm and support vector machine (SVM) and one informative learning method-Gaussian mixture models (GMM). We also discuss the combination of the two types of learning and give the corresponding experiments results.