ICML 2018oral27 citations
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Kejun Huang, Xiao Fu, Nicholas Sidiropoulos
Abstract
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable for cases where the available sample size is large enough to accurately estimate second-order output probabilities, but not higher-order ones. We show that if one is only able to obtain a reliable estimate of the pairwise co-occurrence probabilities of the emissions, it is still possible to uniquely identify the HMM if the emission probability is
BibTeX
@InProceedings{pmlr-v80-huang18c,
title = {Learning Hidden {M}arkov Models from Pairwise Co-occurrences with Application to Topic Modeling},
author = {Huang, Kejun and Fu, Xiao and Sidiropoulos, Nicholas},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2068--2077},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/huang18c/huang18c.pdf},
url = {https://proceedings.mlr.press/v80/huang18c.html},
abstract = {We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable for cases where the available sample size is large enough to accurately estimate second-order output probabilities, but not higher-order ones. We show that if one is only able to obtain a reliable estimate of the pairwise co-occurrence probabilities of the emissions, it is still possible to uniquely identify the HMM if the emission probability is