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ICASSP
2009
IEEE
14 years 13 days ago
Principal component analysis in decomposable Gaussian graphical models
We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. ...
Ami Wiesel, Alfred O. Hero III
ICMLA
2008
13 years 10 months ago
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...
NIPS
2007
13 years 10 months ago
Convex Relaxations of Latent Variable Training
We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while eliminating local minima. First, a cautionary resu...
Yuhong Guo, Dale Schuurmans
UAI
2008
13 years 10 months ago
Strategy Selection in Influence Diagrams using Imprecise Probabilities
This paper describes a new algorithm to solve the decision making problem in Influence Diagrams based on algorithms for credal networks. Decision nodes are associated to imprecise...
Cassio Polpo de Campos, Qiang Ji
MOC
1998
109views more  MOC 1998»
13 years 8 months ago
A comparison of regularizations for an ill-posed problem
Abstract. We consider numerical methods for a “quasi-boundary value” regularization of the backward parabolic problem given by ut + Au = 0 , 0 < t < T u(T ) = f, where A ...
Karen A. Ames, Gordon W. Clark, James F. Epperson,...