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<html>
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<title>
Netlab Reference Manual pca
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<body>
<H1> pca
</H1>
<h2>
Purpose
</h2>
Principal Components Analysis
<p><h2>
Synopsis
</h2>
<PRE>
PCcoeff = pca(data)
PCcoeff = pca(data, N)
[PCcoeff, PCvec] = pca(data)
</PRE>
<p><h2>
Description
</h2>
<CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariance
matrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>. These
coefficients give the variance of <CODE>data</CODE> along the corresponding
principal components.
<p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues.
<p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components as
well as the coefficients. This is considerably more computationally
demanding than just computing the eigenvalues.
<p><h2>
See Also
</h2>
<CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="gtminit.htm">gtminit</a></CODE>, <CODE><a href="ppca.htm">ppca</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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