Overlapped State Hidden Semi-Markov Model for Grouped Multiple Sequences

Hiromi Narimatsu, Hiroyuki Kasai

Abstract: Efficient analysis of multiple sequential data is becoming necessary for identifying sequential patterns of multiple objects of interest. This analysis has major practical and technical importance because finding such patterns necessitates extraction and discovery of latent but meaningful groups of sequences from apparently extraneous but mutually interrelated multiple sequences. However, conventional sequential data analysis methods have not specifically examined this particular technical direction. To tackle this challenge, we propose a new model designated as overlapped state hidden semi-Markov model (OS-HSMM). The model represents the lengths of intervals and overlap among multiple events that are semantically interpretable and appearing across multiple sequences. The salient contribution is that OS-HSMM represents the overlap of two states by extending the state duration probability in HSMM to allow a negative value. Consequently, it handles the state interval and the state overlap simultaneously. Results of our evaluations underscore the effectiveness of our model.

External IDs:dblp:conf/icassp/NarimatsuK20