{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/infossm-interpretable-unsupervised-learning","title":"InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics","arxiv_id":"1809.07109","date":"2018-09-19","proceeding":null,"authors":["Young-Jin Park","Han-Lim Choi"],"abstract":"The goal of system identification is to learn about underlying physics\ndynamics behind the time-series data. To model the probabilistic and\nnonparametric dynamics model, Gaussian process (GP) have been widely used; GP\ncan estimate the uncertainty of prediction and avoid over-fitting. Traditional\nGPSSMs, however, are based on Gaussian transition model, thus often have\ndifficulty in describing a more complex transition model, e.g. aircraft\nmotions. To resolve the challenge, this paper proposes a framework using\nmultiple GP transition models which is capable of describing multi-modal\ndynamics. Furthermore, we extend the model to the information-theoretic\nframework, the so-called InfoSSM, by introducing a mutual information\nregularizer helping the model to learn interpretable and distinguishable\nmultiple dynamics models. Two illustrative numerical experiments in simple\nDubins vehicle and high-fidelity flight simulator are presented to demonstrate\nthe performance and interpretability of the proposed model. Finally, this paper\nintroduces a framework using InfoSSM with Bayesian filtering for air traffic\ncontrol tracking.","url_abs":"http://arxiv.org/abs/1809.07109v2","url_pdf":"http://arxiv.org/pdf/1809.07109v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"infossm-interpretable-unsupervised-learning","repo_url":"https://github.com/yjparkLiCS/InfoSSM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"},{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}