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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Online prediction model based on the SVD-KPCA method.

Ilyes Elaissi¹, Ines Jaffel, Okba Taouali

¹Unité de Recherche d'Automatique, Traitement de Signal et Image (ATSI), Monastir 5000, Tunisia. ilyes.elaissi@yahoo.fr

ISA Transactions

|October 30, 2012

Summary

This study introduces a new SVD-KPCA method for online nonlinear system identification in Reproducing Kernel Hilbert Space (RKHS). It efficiently updates principal components for improved system analysis.

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Area of Science:

Control Systems Engineering
Machine Learning
Signal Processing

Background:

Nonlinear systems require advanced identification techniques.
Reproducing Kernel Hilbert Space (RKHS) offers a powerful framework for nonlinear modeling.
Online identification is crucial for adaptive and real-time control applications.

Purpose of the Study:

To propose a novel method for online identification of nonlinear systems within an RKHS framework.
To enhance the efficiency and accuracy of principal component updates in nonlinear system identification.

Main Methods:

Singular Value Decomposition (SVD) for principal component updates.
Kernel Principal Component Analysis (KPCA) for feature extraction.
Reduced Kernel Principal Component Analysis (RKPCA) for approximating principal components representing selected observations.

Main Results:

The SVD-KPCA method effectively updates principal components in an online manner.
RKPCA provides an efficient approach to approximate key principal components derived from KPCA.
The proposed method demonstrates potential for accurate nonlinear system identification.

Conclusions:

The SVD-KPCA method offers a viable approach for online nonlinear system identification in RKHS.
Combining SVD and KPCA techniques enhances the analysis of complex nonlinear systems.
This method contributes to advancements in adaptive control and system modeling.