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