In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
In this paper, we propose a new nonlinear dimensionality reduction algorithm by adopting regularized least-square criterion on local areas of the data distribution. We first propo...
We investigate the optimality of (1+ )-approximation algorithms obtained via the dimensionality reduction method. We show that: • Any data structure for the (1 + )-approximate n...
Abstract. We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at appl...
Abstract. We present a performance analysis of three linear dimensionality reduction techniques: Fisher's discriminant analysis (FDA), and two methods introduced recently base...