Joint modeling of multiple time series via the beta process with application to motion capture segmentation

TL;DR

This paper introduces a Bayesian nonparametric model using the beta process to jointly analyze multiple related time series, effectively discovering shared behaviors and segmenting sequences without predefined behavior counts.

Contribution

It presents a novel beta process-based approach with advanced MCMC inference for joint time series modeling and segmentation, avoiding model truncation.

Findings

Effective segmentation of human motion capture data

Flexible discovery of shared dynamical behaviors

Efficient MCMC inference with split-merge moves

Abstract

We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the behavior set and the sharing pattern are both inferred from data. We develop Markov chain Monte Carlo (MCMC) methods based on the Indian buffet process representation of the predictive distribution of the beta process. Our MCMC inference algorithm efficiently adds and removes behaviors via novel split-merge moves as well as data-driven birth and death proposals, avoiding the need to consider a truncated model. We demonstrate promising results on unsupervised segmentation of human motion capture data.