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Symplectic Principal Component Analysis : A New Method for Time Series Analysis
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-12-25
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Experimental data are often very complex since the underlying dynamical system may be unknown and the data may heavily be corrupted by noise.
It is a crucial task to properly analyze data to get maximal information of the underlying dynamical system.
This paper presents a novel principal component analysis (PCA) method based on symplectic geometry, called symplectic PCA (SPCA), to study nonlinear time series.
Being nonlinear, it is different from the traditional PCA method based on linear singular value decomposition (SVD).
It is thus perceived to be able to better represent nonlinear, especially chaotic data, than PCA.
Using the chaotic Lorenz time series data, we show that this is indeed the case.
Furthermore, we show that SPCA can conveniently reduce measurement noise.
American Psychological Association (APA)
Lei, Min& Meng, Guang. 2011. Symplectic Principal Component Analysis : A New Method for Time Series Analysis. Mathematical Problems in Engineering،Vol. 2011, no. 2011, pp.1-14.
https://search.emarefa.net/detail/BIM-498573
Modern Language Association (MLA)
Lei, Min& Meng, Guang. Symplectic Principal Component Analysis : A New Method for Time Series Analysis. Mathematical Problems in Engineering No. 2011 (2011), pp.1-14.
https://search.emarefa.net/detail/BIM-498573
American Medical Association (AMA)
Lei, Min& Meng, Guang. Symplectic Principal Component Analysis : A New Method for Time Series Analysis. Mathematical Problems in Engineering. 2011. Vol. 2011, no. 2011, pp.1-14.
https://search.emarefa.net/detail/BIM-498573
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498573