Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences

Abstract : In this study, we propose a method called sequence breaking framework to search high local maximum of the likelihood by providing starting values based on the observations for the Expectation-Maximization algorithm, for Hidden semi-Markov model parameter estimation. The method is shown to be efficient on several datasets with multiple categorical sequences.
Document type :
Conference papers
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal.inria.fr/hal-02129122
Contributor : Brice Olivier <>
Submitted on : Tuesday, May 14, 2019 - 3:55:21 PM
Last modification on : Wednesday, May 22, 2019 - 4:04:20 PM

File

olivier2019assessment.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02129122, version 1

Collections

Citation

Brice Olivier, Anne Guérin-Dugué, Jean-Baptiste Durand. Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences. JdS 2019 - 51èmes Journées de Statistique, Jun 2019, Vandœuvre-lès-Nancy, France. pp.1-7. ⟨hal-02129122⟩

Share

Metrics

Record views

84

Files downloads

407