Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
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...
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
In this paper, we propose the concept of Manifold of Facial Expression based on the observation that images of a subject’s facial expressions define a smooth manifold in the hig...
Spectral methods for nonlinear dimensionality reduction (NLDR) impose a neighborhood graph on point data and compute eigenfunctions of a quadratic form generated from the graph. W...