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» Scalable robust hypothesis tests using graphical models
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ICASSP
2011
IEEE
12 years 7 months ago
Scalable robust hypothesis tests using graphical models
Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many app...
Divyanshu Vats, Vishal Monga, Umamahesh Srinivas, ...
TSP
2010
12 years 10 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
BMVC
2002
13 years 6 months ago
Randomized RANSAC with T(d, d) test
Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The RANSAC al...
Jiri Matas, Ondrej Chum
PRL
2006
117views more  PRL 2006»
13 years 3 months ago
Feature selection in robust clustering based on Laplace mixture
A wrapped feature selection process is proposed in the context of robust clustering based on Laplace mixture models. The clustering approach we consider is a generalization of the...
Aurélien Cord, Christophe Ambroise, Jean Pi...
INFORMATICALT
2011
112views more  INFORMATICALT 2011»
12 years 10 months ago
The Minimum Density Power Divergence Approach in Building Robust Regression Models
It is well known that in situations involving the study of large datasets where influential observations or outliers maybe present, regression models based on the Maximum Likeliho...
Alessandra Durio, Ennio Davide Isaia