PLS Leads to Different Algorithms for Factor Analysis and Regression

Holcomb, Tyler and Morari, Manfred (1993) PLS Leads to Different Algorithms for Factor Analysis and Regression. Technical Report. California Institute of Technology, Pasadena, CA. [CaltechCDSTR:1993.003]

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Abstract

Two multivariable problems of general interest, are factor analysis and regression. This paper examines partial least squares (PLS) as a tool for both problems. For single output data sets, the familiar PLS algorithm is applicable to both problems. For multiple output problems the familiar PLS algorithm [1, 2, 3] (called fact-PLS in this paper) is appropriate for factor analysis. However fact-PLS leads to algebraically-inconistent results for regression problems. To address this issue, a new algebraically-consistent multivariable PLS algorithm, C-PLS, is developed. Unlike fact-PLS, C-PLS does not rely on iterative calculations. Another PLS approach, "one-at-a-time" PLS (OAT-PLS), is closely related to C-PLS; however OAT-PLS is also algebraically-inconsistent. A simulation study of these various PLS methods shows C-PLS to have the best estimation and prediction performance.

EPrint Type:	Monograph (Technical Report)
Additional Information:	Partiul support of this research through the Department of Energy, Office of Basic Energy Scicnces is gratefuly acknowledged.
Subjects:	All Records
ID Code:	70
Deposited By:	Caltech Library System
Deposited On:	23 July 2006
Unique Identifier:	CaltechCDSTR:1993.003
Official Persistent URL:	http://resolver.caltech.edu/CaltechCDSTR:1993.003
Usage Policy:	You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.

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