One-step kernelized sparse clustering on grassmann manifolds

Wen-Bo Hu, Xiaojun Wu, Tianyang Xu

Abstract: Sparse Subspace Clustering (SSC) based clustering methods have achieved great success since these methods could effectively explore the low-dimensional subspace structure embedded in the original data. However, most existing subspace clustering methods are designed for vectorial data from linear spaces, thus not suitable for high dimensional data (such as imageset or video) with the non-linear manifold structure. In this paper, we propose a unified framework about kernelized sparse subspace clustering on Grassmann manifolds, which can learn the optimal affinity graph with the best clustering index matrix. The experimental results on six public datasets illustrate that the proposed method is obviously better than most related clustering methods based on Grassmann manifolds.