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<html>
<head>
<title>
Netlab Reference Manual ppca
</title>
</head>
<body>
<H1> ppca
</H1>
<h2>
Purpose
</h2>
Probabilistic Principal Components Analysis
<p><h2>
Synopsis
</h2>
<PRE>
[var, U, lambda] = pca(x, ppca_dim)
</PRE>
<p><h2>
Description
</h2>
<CODE>[var, U, lambda] = ppca(x, ppca_dim)</CODE> computes the principal component
subspace <CODE>U</CODE> of dimension <CODE>ppca_dim</CODE> using a centred
covariance matrix <CODE>x</CODE>. The variable <CODE>var</CODE> contains
the off-subspace variance (which is assumed to be spherical), while the
vector <CODE>lambda</CODE> contains the variances of each of the principal
components. This is computed using the eigenvalue and eigenvector
decomposition of <CODE>x</CODE>.
<p><h2>
See Also
</h2>
<CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="pca.htm">pca</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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