Subspace clustering (also called projected clustering) addresses the problem that different sets of attributes may be relevant for different clusters in high dimensional feature sp...
A paradigm for music expression understanding based on a joint semantic space, described by both affective and sensorial adjectives, is presented. Machine learning techniques were...
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of...
— Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been...