Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders

Kevin Bascol (1) , Rémi Emonet (1) , Elisa Fromont (1) , Jean-Marc Odobez (2)

1 LHC - Laboratoire Hubert Curien
2 IDIAP Research Institute

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 autoen-coders 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 adap-tive 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.

Domaines: Apprentissage [cs.LG]

Kevin Bascol, Rémi Emonet, Elisa Fromont, Jean-Marc Odobez. Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders. The joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016), Nov 2016, Merida, Mexico. ⟨hal-01374576⟩