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Authors: Joachim Sicking 1 ; 2 ; Maximilian Pintz 2 ; 3 ; Maram Akila 2 and Tim Wirtz 1 ; 2

Affiliations: 1 Fraunhofer Center for Machine Learning, Sankt Augustin, Germany ; 2 Fraunhofer IAIS, Sankt Augustin, Germany ; 3 University of Bonn, Bonn, Germany

Keyword(s): Hidden Markov Model, Representation Learning, Gradient Descent Optimization.

Abstract: We propose DenseHMM – a modification of Hidden Markov Models (HMMs) that allows to learn dense representations of both the hidden states and the (discrete) observables. Compared to the standard HMM, transition probabilities are not atomic but composed of these representations via kernelization. Our approach enables constraint-free and gradient-based optimization. We propose two optimization schemes that make use of this: a modification of the Baum-Welch algorithm and a direct co-occurrence optimization. The latter one is highly scalable and comes empirically without loss of performance compared to standard HMMs. We show that the non-linearity of the kernelization is crucial for the expressiveness of the representations. The properties of the DenseHMM like learned co-occurrences and log-likelihoods are studied empirically on synthetic and biomedical datasets.

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Paper citation in several formats:
Sicking, J., Pintz, M., Akila, M. and Wirtz, T. (2022). DenseHMM: Learning Hidden Markov Models by Learning Dense Representations. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 237-248. DOI: 10.5220/0010821800003122

@conference{icpram22,
author={Joachim Sicking and Maximilian Pintz and Maram Akila and Tim Wirtz},
title={DenseHMM: Learning Hidden Markov Models by Learning Dense Representations},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={237-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010821800003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - DenseHMM: Learning Hidden Markov Models by Learning Dense Representations
SN - 978-989-758-549-4
IS - 2184-4313
AU - Sicking, J.
AU - Pintz, M.
AU - Akila, M.
AU - Wirtz, T.
PY - 2022
SP - 237
EP - 248
DO - 10.5220/0010821800003122
PB - SciTePress

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