Skip to Main content Skip to Navigation
Conference papers

Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series

Abstract : Recent advances in nonlinear Independent Component Analysis (ICA) provide a principled framework for unsupervised feature learning and disentanglement. The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index. This requires manual segmentation of data into non-stationary segments which is computationally expensive, inaccurate and often impossible. These models are thus not fully unsupervised. We remedy these limitations by combining nonlinear ICA with a Hidden Markov Model, resulting in a model where a latent state acts in place of the observed segment-index. We prove identifiability of the proposed model for a general mixing nonlinearity, such as a neural network. We also show how maximum likelihood estimation of the model can be done using the expectation-maximization algorithm. Thus, we achieve a new nonlinear ICA framework which is unsupervised, more efficient, as well as able to model underlying temporal dynamics.
Document type :
Conference papers
Complete list of metadata
Contributor : Aapo Hyvärinen Connect in order to contact the contributor
Submitted on : Tuesday, December 1, 2020 - 4:28:06 PM
Last modification on : Monday, December 13, 2021 - 9:16:11 AM

Links full text


  • HAL Id : hal-03034193, version 1
  • ARXIV : 2006.12107


Hermanni Hälvä, Aapo Hyvärinen. Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. UAI 2020 - Conference on Uncertainty in Artificial Intelligence, Aug 2020, Toronto / Virtual, Canada. ⟨hal-03034193⟩



Les métriques sont temporairement indisponibles