Abstract

We propose a new technique which incrementally derive discriminating features in the input space. This technique casts both classification problems (class labels as outputs) and regression problems (numerical values as outputs) into a unified regression problem. The virtual labels are formed by clustering in the output space. We use these virtual labels to extract discriminating features in the input space. This procedure is performed recursively. We organize the resulting discriminating subspace in a coarse-to-fine fashion and store the information in a decision tree. Such an incrementally hierarchical discriminating regression (IHDR) decision tree can be realized as a hierarchical probability distribution model. We also introduce a sample size dependent negativelog -likelihood (NLL) metric to deal with large-sample size cases, small-sample size cases, and unbalanced-sample size cases. This is very essential since the number of training samples per class are different at each internal node of the IHDR tree. We report experimental results for two types of data: face image data along with comparison with some major appearance-based method and decision trees, hall way images with driving directions as outputs for the automatic navigation problem -- a regression application.

Citations