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<html><head><title>Netlab Reference Manual pca</title></head><body><H1> pca</H1><h2>Purpose</h2>Principal Components Analysis<p><h2>Synopsis</h2><PRE>PCcoeff = pca(data)PCcoeff = pca(data, N)[PCcoeff, PCvec] = pca(data)</PRE><p><h2>Description</h2><CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariancematrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>. Thesecoefficients give the variance of <CODE>data</CODE> along the corresponding principal components. <p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues.<p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components aswell as the coefficients. This is considerably more computationallydemanding than just computing the eigenvalues.<p><h2>See Also</h2><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><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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