Analysis of Nonlinear Partial Least Squares Algorithms

S. Kumar; U. Kruger; E.B. Martin; A.J. Morris

doi:10.1016/S1474-6670(17)31898-0

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Analysis of Nonlinear Partial Least Squares Algorithms

Journal article

Peer reviewed

Analysis of Nonlinear Partial Least Squares Algorithms

S. Kumar, U. Kruger, E.B. Martin and A.J. Morris

IFAC Proceedings Volumes, Vol.37(9), pp.739-744

07/2004

DOI: 10.1016/S1474-6670(17)31898-0

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Abstract

This paper presents an analysis of nonlinear extensions to Partial Least Squares (PLS) using error-based minimization techniques. The analysis revealed that such algorithms are maximizing the accuracy with which the response variables are predicted. Therefore, such algorithms are nonlinear reduced rank regression algorithms rather than nonlinear PLS algorithms

Gradient Methods

Identification Algorithms

Models

Nonlinear Systems

Prediction

Details

Title: Subtitle: Analysis of Nonlinear Partial Least Squares Algorithms
Creators: S. Kumar - University of Newcastle Australia
U. Kruger - Control Group
E.B. Martin - University of Newcastle Australia
A.J. Morris - University of Newcastle Australia
Resource Type: Journal article
Publication Details: IFAC Proceedings Volumes, Vol.37(9), pp.739-744
DOI: 10.1016/S1474-6670(17)31898-0
ISSN: 1474-6670
Language: English
Date published: 07/2004
Academic Unit: Neurosurgery
Record Identifier: 9984304037702771

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