Nonlinear subspace clustering for image clustering

Abstract

We present in this paper a nonlinear subspace clustering (NSC) method for image clustering. Unlike most existing subspace clustering methods which only exploit the linear relationship of samples to learn the affine matrix, our NSC reveals the multi-cluster nonlinear structure of samples via a nonlinear neural network. While kernel-based clustering methods can also address the nonlinear issue of samples, this type of methods suffers from the scalability issue. Specifically, our NSC employs a feed-forward neural network to map samples into a nonlinear space and performs subspace clustering at the top layer of the network, so that the mapping functions and the clustering issues are iteratively learned. Otherwise, our NSC applys a similarity measure based on the grouping effect to capture the local structure of data. Experimental results illustrate that our NSC outperforms the state-of-the-arts.