We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure f...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
In many real-world classification problems the input contains a large number of potentially irrelevant features. This paper proposes a new Bayesian framework for determining the r...
Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picar...
In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model...
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to se...
Marco Cristani, Umberto Castellani, Vittorio Murin...