Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10618-019-00639-x | Data Mining and Knowledge Discovery |
Keywords | Field | DocType |
Koopman operators, Dynamic data mining, Data-driven methods, Dimensionality reduction, Spectral methods, Time-series classification, Healthcare, Bioinformatics | Linear dynamical system,Data mining,Dimensionality reduction,Linear system,Data analysis,Computer science,Support vector machine,Dynamic data,Information extraction,Artificial intelligence,Knowledge extraction,Machine learning | Journal |
Volume | Issue | ISSN |
33 | 6 | 1384-5810 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhang | 1 | 0 | 1.35 |
| Yao-Chi Yu | 2 | 0 | 0.34 |
| Shin Li Jr. | 3 | 112 | 19.45 |