Confused Distance Maximization for Large Category Dimensionality Reduction

Abstract

The Fisher linear discriminant analysis (FDA) is the most well-known supervised dimensionality reduction model. However, when the number of classes is much larger than the reduced dimensionality, FDA suffers from the class separation problem in that it will preserve the distances of the already well-separated classes and cause a large overlap of neighboring classes. To cope with this problem, we propose a new model called confused distance maximization (CDM). The objective of CDM is to maximize the distance of the most confusable classes, according to the confusion matrix estimated from the training data with a pre-learned classifier. Compared with FDA that maximizes the sum of the distances of all class pairs, CDM is more relevant to the classification accuracy by weighting the pairwise distance according to the confusion matrix. Furthermore, CDM is computationally inexpensive which makes it indeed efficient and effective for large category problems. Experiments on two large-scale 3,755-class Chinese handwriting databases (offline and online) demonstrate that CDM can achieve the best performance compared with FDA and other competitive weighting based criteria.