79 search results - page 7 / 16 » Bayesian Maximum Margin Principal Component Analysis |
ICA
2004
Springer
2004
Springer
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
15 years 3 months ago
The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Becau...
101
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ICA
2007
Springer
15 years 4 months ago
2007
Springer
Functional Data Analysis (FDA) is used for datasets that are more meaningfully represented in the functional form. Functional principal component analysis, for instance, is used to...
101
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SIBGRAPI
2005
IEEE
15 years 4 months ago
2005
IEEE
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recog...
TIP
2011
14 years 5 months ago
2011
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
CVPR
2003
IEEE
16 years 14 days ago
2003
IEEE
We present a Bayesian approach to image-based visual hull reconstruction. The 3-D shape of an object of a known class is represented by sets of silhouette views simultaneously obs...