Laplace equations

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).

Linear Laplacian Discrimination for Feature Extraction

We propose the linear Laplacian discrimination (LLD) algorithm/or discriminant feature extraction, which is an extension of linear discriminant analysis (LDA). Our motivation is to address the issue that LDA cannot work well in cases where sample spaces are non-Euclidean.