Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
In this paper, an integrated resolution up-conversion and compression artifacts removal algorithm is proposed. Local image patterns are classified into object details or coding ar...
Semi-definite Embedding (SDE) has been a recently proposed to maximize the sum of pair wise squared distances between outputs while the input data and outputs are locally isometri...
Benyu Zhang, Jun Yan, Ning Liu, QianSheng Cheng, Z...
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...