Continual Learning for Infinite Hierarchical Change-Point Detection
Abstract
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is to infer the posterior distribution of the change-point locations. However, for complex models (high-dimensional or heterogeneous), it is not possible to perform reliable detection. To circumvent this problem, we propose to use a hierarchical model, which yields observations that belong to a lower-dimensional manifold. Concretely, we consider a latent-class model with an unbounded number of categories, which is based on the chinese-restaurant process (CRP). For this model we derive a continual learning mechanism that is based on the sequential construction of the CRP and the expectation-maximization (EM) algorithm with a stochastic maximization step. Our results show that the proposed method is able to recursively infer the number of underlying latent classes and perform CPD in a reliable manner.
Cite
@article{arxiv.1910.10087,
title = {Continual Learning for Infinite Hierarchical Change-Point Detection},
author = {Pablo Moreno-Muñoz and David Ramírez and Antonio Artés-Rodríguez},
journal= {arXiv preprint arXiv:1910.10087},
year = {2019}
}