The Segmented iHMM: A simple, efficient hierarchical infinite HMM

Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme. The siHMM is well suited to segmentation problems, where the goal is to identify points at which a time series transitions from one relatively stable regime to a new regime. Conventional iHMMs often struggle with such problems, since they have no mechanism for distinguishing between high- and low-level dynamics. Hierarchical HMMs (HHMMs) can do better, but they require much more complex and expensive inference algorithms. The siHMM retains the simplicity and efficiency of the iHMM, but out-performs it on a variety of segmentation problems, achieving performance that matches or ex-ceeds that of a more complicated HHMM.

Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsKilian Q. Weinberger, Maria Florina Balcan
PublisherInternational Machine Learning Society (IMLS)
Pages3949-3959
Number of pages11
ISBN (Electronic)9781510829008
StatePublished - Jan 1 2016
Externally publishedYes
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Publication series

Name33rd International Conference on Machine Learning, ICML 2016
Volume6

Other

Other33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period6/19/166/24/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'The Segmented iHMM: A simple, efficient hierarchical infinite HMM'. Together they form a unique fingerprint.

  • Cite this

    Saeedi, A., Hoffman, M., Johnson, M., & Adams, R. (2016). The Segmented iHMM: A simple, efficient hierarchical infinite HMM. In K. Q. Weinberger, & M. F. Balcan (Eds.), 33rd International Conference on Machine Learning, ICML 2016 (pp. 3949-3959). (33rd International Conference on Machine Learning, ICML 2016; Vol. 6). International Machine Learning Society (IMLS).