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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)
+
+
+</body>
+</html>
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