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From: saswss@hotellng.unx.sas.com (Warren Sarle)
Subject: Re: Is PCA the same as DA?
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Date: Mon, 10 Mar 1997 22:54:33 GMT
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In article <5fu1vo$el8@wapping.ecs.soton.ac.uk>, aps@ecs.soton.ac.uk (Adrian Smith) writes:
|> Can anyone give (direct me to) a not-too-mathematical description
|> of Principal Component Analysis and Discriminant Analysis?

Principal component analysis (PCA) is an unsupervised data-compression
technique that possesses numerous marvelous optimality properties.
Discriminant analysis is any method for statistical pattern recognition
and therefore includes many techniques.

References for PCA:

   Deco, G. and Obradovic, D. (1996), An Information-Theoretic
   Approach to Neural Computing, NY: Springer-Verlag.

   Diamantaras, K.I., and Kung, S.Y. (1996) Principal Component Neural
   Networks: Theory and Applications, NY: Wiley.

   Hotelling, H. (1933), "Analysis of a Complex of Statistical
   Variables into Principal Components," Journal of Educational
   Psychology, 24, 417-441, 498-520.

   Jackson, J.E. (1991) A User's Guide to Principal Components, Wiley.

   Jolliffe, I.T. (1986) Principal Component Analysis,
   Springer-Verlag.

   Pearson, K. (1901) "On Lines and Planes of Closest Fit to Systems
   of Points in Space," Phil. Mag., 2(6), 559-572.

   Rao, C.R. (1964), "The Use and Interpretation of Principal
   Component Analysis in Applied Research," Sankya A, 26, 329-358.

References for discriminant analysis:

   Hand, D.J. (1981) Discrimination and Classification, Wiley: NY.

   McLachlan, G.J. (1992) Discriminant Analysis and Statistical Pattern
   Recognition, Wiley: NY.

   Michie, D., Spiegelhalter, D.J. and Taylor, C.C. (1994), Machine
   Learning, Neural and Statistical Classification, Ellis Horwood.

   Ripley, B.D. (1996) Pattern Recognition and Neural Networks, 
   Cambridge: Cambridge University Press, ISBN 0-521-46086-7.

   Weiss, S.M. & Kulikowski, C.A. (1991), Computer Systems That
   Learn, Morgan Kaufmann.


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Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
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