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...
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Abstract. We generalize traditional goals of clustering towards distinguishing components in a non-parametric mixture model. The clusters are not necessarily based on point locatio...
Stefanie Jegelka, Arthur Gretton, Bernhard Sch&oum...
Abstract. This paper presents an innovative, adaptive variant of Kohonen’s selforganizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on ...
Abstract. Planar drawings of clustered graphs are considered. We introduce the notion of completely connected clustered graphs, i.e. hierarchically clustered graphs that have the p...