79 search results - page 6 / 16 » Bayesian Maximum Margin Principal Component Analysis |
ECCV
2000
Springer
16 years 9 days ago
2000
Springer
In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or `subspaces', of natural images. Examples include principal component anal...
CSDA
2008
14 years 10 months ago
2008
A Bayesian approach to analyze the modes of variation in a set of curves is suggested. It is based on a generative model thus allowing for noisy and sparse observations of curves....
77
Voted
IPMI
2003
Springer
15 years 11 months ago
2003
Springer
The Gaussian distribution is the basis for many methods used in the statistical analysis of shape. One such method is principal component analysis, which has proven to be a powerfu...
ICML
2006
IEEE
15 years 11 months ago
2006
IEEE
Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, p...
ICPR
2006
IEEE
15 years 11 months ago
2006
IEEE
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...