31 search results - page 1 / 7 » Principal Manifolds and Bayesian Subspaces for Visual Recogn... |
ICCV
1999
IEEE
14 years 3 months ago
1999
IEEE
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
PAMI
2002
13 years 10 months ago
2002
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
PR
2007
13 years 10 months ago
2007
In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particula...
ICCV
2003
IEEE
15 years 28 days ago
2003
IEEE
Previous works have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analy...
IPAS
2010
13 years 9 months ago
2010
In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same...