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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual ppca
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> ppca
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Probabilistic Principal Components Analysis
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 [var, U, lambda] = pca(x, ppca_dim)
wolffd@0 20 </PRE>
wolffd@0 21
wolffd@0 22
wolffd@0 23 <p><h2>
wolffd@0 24 Description
wolffd@0 25 </h2>
wolffd@0 26
wolffd@0 27 <CODE>[var, U, lambda] = ppca(x, ppca_dim)</CODE> computes the principal component
wolffd@0 28 subspace <CODE>U</CODE> of dimension <CODE>ppca_dim</CODE> using a centred
wolffd@0 29 covariance matrix <CODE>x</CODE>. The variable <CODE>var</CODE> contains
wolffd@0 30 the off-subspace variance (which is assumed to be spherical), while the
wolffd@0 31 vector <CODE>lambda</CODE> contains the variances of each of the principal
wolffd@0 32 components. This is computed using the eigenvalue and eigenvector
wolffd@0 33 decomposition of <CODE>x</CODE>.
wolffd@0 34
wolffd@0 35 <p><h2>
wolffd@0 36 See Also
wolffd@0 37 </h2>
wolffd@0 38 <CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="pca.htm">pca</a></CODE><hr>
wolffd@0 39 <b>Pages:</b>
wolffd@0 40 <a href="index.htm">Index</a>
wolffd@0 41 <hr>
wolffd@0 42 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 43
wolffd@0 44
wolffd@0 45 </body>
wolffd@0 46 </html>