Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection
Proceedings of the Asian Conference on Machine Learning, PMLR 25:113-128, 2012.
Online algorithms allow data instances to be processed in a sequential way, which is important for large-scale and real-time applications. In this paper, we propose a novel online clustering approach based on a Dirichlet process mixture of generalized Dirichlet (GD) distributions, which can be considered as an extension of the finite GD mixture model to the infinite case. Our approach is built on nonparametric Bayesian analysis where the determination of the number of clusters is sidestepped by assuming an infinite number of mixture components. Moreover, an unsupervised localized feature selection scheme is integrated with the proposed nonparametric framework to improve the clustering performance. By learning the proposed model in an online manner using a variational approach, all the involved parameters and features saliencies are estimated simultaneously and effectively in closed forms. The proposed online infinite mixture model is validated through both synthetic data sets and two challenging real-world applications namely text document clustering and online human face detection.