Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models

Hidden semi-Markov models

Article history: Received 14 April 2009 Available online 17 November 2009

Hidden semi-Markov models (HSMMs)

A semi-Markov HMM (more properly called a hidden semi-Markov model, or HSMM) is like an HMM except each state can emit a sequence of observations. Let Y (Gt) be the subsequence emitted by “generalized state” Gt. The “generalized state” usually contains both the automaton state, Qt, and the length (duration) of the segment, Lt. We will define Y (Gt) to be the subsequence yt−l+1:t. After emitting...

Optimal linear feature transformations for semi-continuous hidden Markov models

Linear discriminant or Karhunen-Lo eve transforms are established techniques for mapping features into a lower dimensional subspace. This paper introduces a uniform statistical framework, where the computation of the optimal feature reduction is formalized as a Maximum-Likelihood estimation problem. The experimental evaluation of this suggested extension of linear selection methods shows a slig...

Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Hierarchical Multi-channel Hidden Semi Markov Models

Many interesting human actions involve multiple interacting agents and also have typical durations. Further, there is an inherent hierarchical organization of these activities. In order to model these we introduce a new family of hidden Markov models (HMMs) that provide compositional state representations in both space and time and also a recursive hierarchical structure for inference at higher...