79 search results - page 2 / 16 » Bayesian Maximum Margin Principal Component Analysis |
PAMI
2002
13 years 4 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...
JMLR
2010
12 years 11 months ago
2010
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
AI
2005
Springer
13 years 4 months ago
2005
Springer
Abstract. The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for ...
JMLR
2010
12 years 11 months ago
2010
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
ICCV
1999
IEEE
13 years 9 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...