A Caltech Library Service

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. California Institute of Technology , Pasadena, CA. (Unpublished)

[img] Postscript
See Usage Policy.

See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Report or Paper (Technical Report)
Additional Information:Partiul support of this research through the Department of Energy, Office of Basic Energy Scicnces is gratefuly acknowledged.
Group:Control and Dynamical Systems Technical Reports
Record Number:CaltechCDSTR:1993.003
Persistent URL:
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28045
Deposited By: Imported from CaltechCDSTR
Deposited On:23 Jul 2006
Last Modified:03 Oct 2019 03:28

Repository Staff Only: item control page