Joint Problems in Learning Multiple Dynamical Systems

Niu, Mengjia; He, Xiaoyu; Ryšavý, Petr; Zhou, Quan; Mareček, Jakub

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https://hdl.handle.net/2142/130321 Copy

Description

Title

Joint Problems in Learning Multiple Dynamical Systems

Author(s)

• Niu, Mengjia
• He, Xiaoyu
• Ryšavý, Petr
• Zhou, Quan
• Mareček, Jakub

Issue Date

2025-09-17

Keyword(s)

• Clustering of time series
• Learning linear dynamical systems
• Joint problems

Abstract

Clustering of time series is a well-studied problem [53], [29], with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory. We consider a variant, where given a set of trajectories and a number of parts, we jointly partition the set of trajectories and learn linear dynamical system (LDS) models for each part, so as to minimize the maximum error across all the models. We present globally convergent methods and EM heuristics, accompanied by promising computational results. The key highlight of this method is that it does not require a predefined hidden state dimension but instead provides an upper bound. Additionally, it offers guidance for determining regularization in the system identification.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130321&&

Copyright and License Information

Copyright 2025 owned by the authors.

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