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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 componentsubspace <CODE>U</CODE> of dimension <CODE>ppca_dim</CODE> using a centredcovariance matrix <CODE>x</CODE>. The variable <CODE>var</CODE> containsthe off-subspace variance (which is assumed to be spherical), while thevector <CODE>lambda</CODE> contains the variances of each of the principalcomponents. 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>⌨️ 快捷键说明
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