Unsupervised feature learning with C-SVDDNet

Abstract

In this paper we present a novel unsupervised feature learning network named C-SVDDNet, a single-layer K-means-based network towards compact and robust feature representation. Our contributions are three folds: (1) we introduce C-SVDD encoding, a generalization of the K-means local encoding that adapts to the distribution information and improves the robustness against outliers; (2) we propose a method that effectively embeds the spatial information of 2D data into the final representation based on a modified SIFT descriptor; and (3) we extend our C-SVDDNet to exploit multi-scale information for better feature learning. Extensive experiments on several popular object recognition benchmarks, such as STL-10, MINST, Holiday and Copydays shows that the proposed method yields comparable or better performance than that of the previous state-of-the-art unsupervised feature learning methods.