Feature Extraction Based on Support Vector Data Description

Motivated by the improvement of performance and reduction of complexity, feature extraction is referred to one manner of dimensionality reduction. This paper presents a new feature extraction method based on support vector data description (FE-SVDD). First, the proposed method establishes hyper-sphere models for each category of the given data using support vector data description. Second, FE-SVDD calculates the distances between data points and the centers of the hyper-spheres. Finally, the ratios of the distances to the radii of the hyper-spheres are treated as new extracted features. Experimental results on different data sets indicate that FE-SVDD can speed up the procedure of feature extraction and extract the distinctive information of original data.