A proposal for supervised clustering with Dirichlet Process using labels
A proposal for supervised clustering with Dirichlet Process using labels
Authors
Peralta Márquez, Billy
Caro, Alberto
Soto, Alvaro
Caro, Alberto
Soto, Alvaro
Authors
Date
Datos de publicación:
10.1016/j.patrec.2016.05.019
Keywords
Collections
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.