Centering SVDD for Unsupervised Feature Representation in Object Classification

Abstract. Learning good feature representation from unlabeled data has attracted researchers great attention recently. Among others, K-means clustering algorithm is popularly used to map the input data into a feature representation, by finding the nearest centroid for each input point. However, this ignores the density information of each cluster com-pletely and the resulting representation may be too terse. In this paper, we proposed a SVDD (Support Vector Data Description) based method to address these issues. The key idea of our method is to use SVDD to measure the density of each cluster resulted from K-Means clustering, based on which a robust feature representation can be derived. For this purpose, we add a new constraint to the original SVDD objective func-tion to make the model align better with the data. In addition, we show that our modified SVDD can be solved very efficiently as a linear pro-gramming problem, instead of as a quadratic one. The effectiveness and feasibility of the proposed method is verified on two object classification databases with promising results