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author wolffd
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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual pca
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> pca
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 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 PCcoeff = pca(data)
wolffd@0 20 PCcoeff = pca(data, N)
wolffd@0 21 [PCcoeff, PCvec] = pca(data)
wolffd@0 22 </PRE>
wolffd@0 23
wolffd@0 24
wolffd@0 25 <p><h2>
wolffd@0 26 Description
wolffd@0 27 </h2>
wolffd@0 28
wolffd@0 29 <CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariance
wolffd@0 30 matrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>. These
wolffd@0 31 coefficients give the variance of <CODE>data</CODE> along the corresponding
wolffd@0 32 principal components.
wolffd@0 33
wolffd@0 34 <p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues.
wolffd@0 35
wolffd@0 36 <p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components as
wolffd@0 37 well as the coefficients. This is considerably more computationally
wolffd@0 38 demanding than just computing the eigenvalues.
wolffd@0 39
wolffd@0 40 <p><h2>
wolffd@0 41 See Also
wolffd@0 42 </h2>
wolffd@0 43 <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>
wolffd@0 44 <b>Pages:</b>
wolffd@0 45 <a href="index.htm">Index</a>
wolffd@0 46 <hr>
wolffd@0 47 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 48
wolffd@0 49
wolffd@0 50 </body>
wolffd@0 51 </html>