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Latent space unsupervised semantic segmentation.

Knut J Strommen1, Jim Tørresen1,2, Ulysse Côté-Allard1,2

  • 1Department of Informatics, University of Oslo, Oslo, Norway.

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Summary

This study introduces Latent Space Unsupervised Semantic Segmentation (LS-USS), a new method for segmenting multidimensional time series data. LS-USS enables real-time analysis of wearable sensor data by effectively detecting change-points in both online and batch streams.

Keywords:
autoencoderbiosignal processingchange-point detection (CPD)machine learningmulti-dimensional time seriessemantic segmentationunsupervised learning

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Area of Science:

  • * Data Science
  • * Signal Processing
  • * Machine Learning

Background:

  • * Wearable sensors generate vast amounts of multidimensional time series biosignals.
  • * Unsupervised data segmentation is crucial for analyzing this data at scale.
  • * Traditional change-point detection methods struggle with real-time and multidimensional data.

Purpose of the Study:

  • * To develop a novel unsupervised segmentation algorithm for multidimensional time series.
  • * To enable real-time and scalable analysis of biosignal data.
  • * To address limitations of existing change-point detection techniques.

Main Methods:

  • * Proposed Latent Space Unsupervised Semantic Segmentation (LS-USS) algorithm.
  • * Utilized an autoencoder to learn a 1D latent space for change-point detection.
  • * Introduced Local Threshold Extraction Algorithm (LTEA) and a 'batch collapse' algorithm for real-time processing.

Main Results:

  • * LS-USS effectively segments multidimensional time series data.
  • * The algorithm performs accurately in both offline and real-time settings.
  • * LS-USS achieved equal or superior performance compared to state-of-the-art methods.

Conclusions:

  • * LS-USS offers a robust solution for unsupervised time series segmentation.
  • * The method is suitable for real-time applications with streaming sensor data.
  • * This work advances the analysis of complex biosignals from wearable devices.