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Title: Mode Clustering for Markov Jump Systems
In this work, we consider the problem of mode clustering in Markov jump models. This model class consists of multiple dynamical modes with a switching sequence that determines how the system switches between them over time. Under different active modes, the observations can have different characteristics. Given the observations only and without knowing the mode sequence, the goal is to cluster the modes based on their transition distributions in the Markov chain to find a reduced-rank Markov matrix that is embedded in the original Markov chain. Our approach involves mode sequence estimation, mode clustering and reduced-rank model estimation, where mode clustering is achieved by applying the singular value decomposition and k-means. We show that, under certain conditions, the clustering error can be bounded, and the reduced-rank Markov chain is a good approximation to the original Markov chain. Through simulations, we show the efficacy of our approach and the application of our approach to real world scenarios. Index Terms—Switched model, Markov chain, clustering  more » « less
Award ID(s):
1845076 1838179
PAR ID:
10137575
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE CAMSAP 2019
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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