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Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders

  • Kevin Bascol
  • Rémi Emonet
  • Elisa FromontEmail author
  • Jean-Marc Odobez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

We study the use of feed-forward convolutional neural networks for the unsupervised problem of mining recurrent temporal patterns mixed in multivariate time series. Traditional convolutional autoencoders lack interpretability for two main reasons: the number of patterns corresponds to the manually-fixed number of convolution filters, and the patterns are often redundant and correlated. To recover clean patterns, we introduce different elements in the architecture, including an adaptive rectified linear unit function that improves patterns interpretability, and a group-lasso regularizer that helps automatically finding the relevant number of patterns. We illustrate the necessity of these elements on synthetic data and real data in the context of activity mining in videos.

Keywords

Mean Square Error Multivariate Time Series Stochastic Gradient Descent Restrict Boltzmann Machine Suppression Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work has been supported by the ANR project SoLStiCe (ANR-13-BS02-0002-01).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kevin Bascol
    • 1
  • Rémi Emonet
    • 1
  • Elisa Fromont
    • 1
    Email author
  • Jean-Marc Odobez
    • 2
  1. 1.Univ Lyon, UJM-Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516Saint-EtienneFrance
  2. 2.Idiap Research InstituteMartignySwitzerland

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