Subspace learning based low-rank representation

Abstract

Subspace segmentation has been a hot topic in the past decades. Recently, spectral-clustering based methods arouse broad interests, however, they usually consider the similarity extraction in the original space. In this paper, we propose subspace learning based low-rank representation to learn a subspace favoring the similarity extraction for the low-rank representation. The process of learning the subspace and achieving the representation is conducted simultaneously and thus they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which is a general case of subspace segmentation. Moreover, our method can also be applied in the problem of recognition by adding suitable penalty on the learned subspace. Extensive experimental results confirm the effectiveness of our method.