Centering SVDD for Unsupervised Feature Representation in Object Classification

Learning good feature representation from unlabeled data has attracted researchers great attention recently. Among others, Kmeans 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 completely 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 function 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 programming 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.