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...
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 ...
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...
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...
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...