• Publications
  • Influence
Locality Preserving Projections
TLDR
These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
Face recognition using Laplacianfaces
TLDR
Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Neighborhood preserving embedding
TLDR
This paper proposes a novel subspace learning algorithm called neighborhood preserving embedding (NPE), which aims at preserving the local neighborhood structure on the data manifold and is less sensitive to outliers than principal component analysis (PCA).
Laplacian Score for Feature Selection
TLDR
This paper proposes a "filter" method for feature selection which is independent of any learning algorithm, based on the observation that, in many real world classification problems, data from the same class are often close to each other.
Unsupervised feature selection for multi-cluster data
TLDR
Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, a new approach is proposed, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection, which select those features such that the multi-cluster structure of the data can be best preserved.
Semi-supervised Discriminant Analysis
TLDR
This paper proposes a novel method, called Semi- supervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples to learn a discriminant function which is as smooth as possible on the data manifold.
Locality Sensitive Discriminant Analysis
TLDR
A novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA), which finds a projection which maximizes the margin between data points from different classes at each local area by discovering the local manifold structure.
Orthogonal Laplacianfaces for Face Recognition
TLDR
An appearance-based face recognition method, called orthogonal Laplacianface, based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold.
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
TLDR
This paper proposes to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values, and develops a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm.
Graph Regularized Nonnegative Matrix Factorization for Data Representation.
TLDR
In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
...
...