Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model

Journal article (2018)

Authors

W. Pei Pattern Recognition and Bioinformatics - EEMCS
H. Dibeklioglu Pattern Recognition and Bioinformatics - EEMCS
David Tax Pattern Recognition and Bioinformatics - EEMCS
L.J.P. van der Maaten Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2018 W. Pei, H. Dibeklioglu, D.M.J. Tax, L.J.P. van der Maaten
DOI: https://doi.org/10.1109/TNNLS.2017.2651018
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series classification such as the hidden
conditional random field, our model can model very complex decision boundaries, because the number of latent states grows exponentially with the number of hidden units. We demonstrate the strong performance of our model in experiments on a variety of (computer vision) tasks, including handwritten character recognition, speech recognition, facial expression, and action recognition. We also present a state-of-the-art system for facial
action unit detection based on the HULM.

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