Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is n...

Classification based on Low-Rank Representation (LRR) has been a hot-topic in the field of pattern classification. However, LRR may not be able to fuse the local and global information of data completely and fail to represent nonlinear samples. In this paper, we propose a kernel locality preserving low-rank representation with Tikhonov regularization (KLP-LRR) for face recognition. KLP-LRR is a...

We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each d...

Low-rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is n...

Dimensionality Reduction is a common way to solve the problem of ‘curse of dimensions’, especially for image processing. Among all these methods, the linear methods are believed to have better performance in actual databases. This paper proposes a novel unsupervised linear dimensionality reduction method that based on low rank representation which aims at finding the subspace structure of the o...