LAUSR

Recently, the Riemannian manifold has received special attention in unsupervised clustering since the real-world visual data usually resides on a special manifold where Euclidean geometry fails to capture. Although many clustering algorithms have been proposed, most of them use only a single geometric model to describe the data. In this paper, a multi-geometric subspace clustering model is proposed, and the subspace representation is learned together by constructing a shared affinity matrix of multi-order data. Experimental results on several different types of datasets show that the clustering performance of our proposed algorithm is better than most of subspaces algorithms.