Improved feature reduction in input and feature spaces

In this paper, we present an improved feature reduction method in input and feature spaces for classification using support vector machines (SVMs). In the input space, we select a subset of input features by ranking their contributions to the decision function. In the feature space, features are ranked according to the weighted support vector in each dimension. By applying feature reduction in both input and feature spaces, we develop a fast non-linear SVM without a significant loss in performance. We have tested the proposed method on the detection of face, person, and car. Subsets of features are chosen from pixel values for face detection and from Haar wavelet features for person and car detection. The experimental results show that the proposed feature reduction method works successfully. In fact, our method performs better than the methods of using all the features and the Fisher's features in the detection of person and car. We also gain the advantage of speed.