An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data

TL;DR

This paper introduces an adaptive online HDP-HMM that effectively segments and classifies streaming sequential data into an unlimited number of classes, adapting to evolving data distributions in real-time.

Contribution

It presents a novel online hierarchical Dirichlet process model with a learning rate for dynamic adaptation, addressing the gap in real-time classification of evolving data streams.

Findings

High accuracy in synthetic data segmentation and classification.

Effective handling of evolving sequences with new classes.

Robust performance on video datasets with changing distributions.

Abstract

In the recent years, the desire and need to understand sequential data has been increasing, with particular interest in sequential contexts such as patient monitoring, understanding daily activities, video surveillance, stock market and the like. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly, without being limited to a rigid number of classes. In addition, the model needs to be capable of updating its parameters to comply with possible evolutions. This interesting problem, however, is not adequately addressed in the literature since many studies focus on offline classification over a pre-defined class set. In this paper, we propose a principled solution to this gap by introducing an adaptive online system based on Markov switching models with hierarchical Dirichlet process priors. This infinite adaptive online approach is capable of segmenting and classifying the sequential data over unlimited number of classes, while meeting the memory and delay constraints of streaming contexts. The model is further enhanced by introducing a learning rate, responsible for balancing the extent to which the model sustains its previous learning (parameters) or adapts to the new streaming observations. Experimental results on several variants of stationary and evolving synthetic data and two video datasets, TUM Assistive Kitchen and collatedWeizmann, show remarkable performance in segmentation and classification, particularly for evolutionary sequences with changing distributions and/or containing new, unseen classes.