LEARNING BASED ON KERNEL DISCRIMINANT-EM ALGORITHM FOR IMAGE CLASSIFICATION

In image classification and other learning-based object recognition tasks, it is often tedious and expensive to label large training data sets. Discriminant-EM (DEM) proposed a semi-supervised learning framework which takes both labeled and unlabeled data to learn classifiers. This paper extends the linear D-EM to nonlinear kernel algorithm, KDEM and evaluates KDEM on both benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated. 1