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Netlab Reference Manual ppca
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<H1> ppca
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<h2>
Purpose
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Probabilistic Principal Components Analysis

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Synopsis
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<PRE>
[var, U, lambda] = pca(x, ppca_dim)
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Description
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<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>.

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See Also
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<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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author	wolffd
date	Tue, 10 Feb 2015 15:05:51 +0000
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