A proposal for supervised clustering with Dirichlet Process using labels

Thumbnail
Authors
Peralta Márquez, Billy
Caro, Alberto
Soto, Alvaro
Authors
Date
Datos de publicación:
PATTERN RECOGNITION LETTERS,Vol.80,52-57,2016
Keywords
Abstract
Supervised clustering is an emerging area of machine learning, where the goal is to find class-uniform clusters. However, typical state-of-the-art algorithms use a fixed number of clusters. In this work, we propose a variation of a non-parametric Bayesian modeling for supervised clustering. Our approach consists of modeling the clusters as a mixture of Gaussians with the constraint of encouraging clusters of points with the same label. In order to estimate the number of clusters, we assume a-priori a countably infinite number of clusters using a variation of Dirichlet Process model over the prior distribution. In our experiments, we show that our technique typically outperforms the results of other clustering techniques. (C) 2016 Elsevier B.V. All rights reserved.
Description
Journal Volumes
Journals
Journal Issues
relationships.isJournalVolumeOf
relationships.isArticleOf
Journal Issue
Organizational Units
relationships.isArticleOf
Organizational Units
relationships.isPersonaOf
Organizational Units
relationships.isTesisOfOrg