This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the amb...
This paper examines the problem of extracting lowdimensional manifold structure given millions of highdimensional face images. Specifically, we address the computational challenge...
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
This paper investigates compression of 3D objects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object'...
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...