Low Rank Representation

Label Information Guided Graph Construction for Semi-Supervised Learning

We propose a novel semi-supervised graph learning method called semi-supervised low-rank representation, which results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method.

Locality-Preserving Low-Rank Representation for Graph Construction from Nonlinear Manifolds

We proposes a novel method to learn an undirected graph from a mixture of nonlinear manifolds via Locality-Preserving Low-Rank Representation (), which extents the original LRR model from linear subspaces to nonlinear manifolds.

Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion

We formulate the tag completion problem in a subspace clustering model which assumes that images are sampled from subspaces, and complete the tags using the state-of-the-art Low Rank Representation (LRR) method.

Robust Recovery of Subspace Structures by Low-Rank Representation

we propose a novel objective function named Low-Rank Representation (LRR), which seeks the lowest rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary.

Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation

We propose a linearized alternating direction method with adaptive penalty for solving subproblems in ADM conveniently

Robust Subspace Segmentation by Low-Rank Representation

In this work we propose the low-rank representation (LRR) to recover the lowest-rank representation of a set of data vectors in a joint way, i.e., to recover the lowest-rank representation of matrix data.