Latent Space Unsupervised Semantic Segmentation

TL;DR

This paper introduces LS-USS, a new unsupervised algorithm for segmenting multidimensional time series, capable of real-time and offline analysis, outperforming existing change-point detection methods on various datasets.

Contribution

The paper presents LS-USS, a novel unsupervised segmentation method for multidimensional time series that works effectively in both online and batch modes.

Findings

LS-USS performs on par or better than state-of-the-art algorithms.

It is suitable for real-time applications.

It handles multidimensional time series effectively.

Abstract

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.