Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is d...
In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial waveletbased denoising techniques, based on Bayesian leastsquares optimization...
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In th...
In this paper, we propose a robust model selection criterion for mixtures of subspaces called minimum effective dimension (MED). Previous information-theoretic model selection cri...