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Dynamics reconstruction and classification via Koopman features

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Abstract

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop a novel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems.

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Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, USA

    Wei Zhang, Yao-Chi Yu & Jr-Shin Li

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  1. Wei Zhang
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  2. Yao-Chi Yu
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  3. Jr-Shin Li
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Correspondence to Jr-Shin Li.

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Responsible editor: Eamonn Keogh.

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This work was supported in part by the National Science Foundation under the awards CMMI-1763070 and ECCS-1509342 and the National Institutes of Health Grants R21EY027590 and R01GM131403.

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Zhang, W., Yu, YC. & Li, JS. Dynamics reconstruction and classification via Koopman features. Data Min Knowl Disc 33, 1710–1735 (2019). https://doi.org/10.1007/s10618-019-00639-x

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