Dual Graph Regularized Latent Low-rank Representation for Subspace Clustering

We propose a dual graph regularized LRR model (DGLRR) by enforcing preservation of geometric information in both the ambient space and the feature space.

Laplacian Regularized Low-Rank Representation and Its Applications

We propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR).

A Regularized Approach for Geodesic Based Semi-Supervised Multi-Manifold Learning

We regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed.