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comparison toolboxes/FullBNT-1.0.7/nethelp3.3/ppca.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual ppca | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> ppca | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Probabilistic Principal Components Analysis | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 [var, U, lambda] = pca(x, ppca_dim) | |
| 20 </PRE> | |
| 21 | |
| 22 | |
| 23 <p><h2> | |
| 24 Description | |
| 25 </h2> | |
| 26 | |
| 27 <CODE>[var, U, lambda] = ppca(x, ppca_dim)</CODE> computes the principal component | |
| 28 subspace <CODE>U</CODE> of dimension <CODE>ppca_dim</CODE> using a centred | |
| 29 covariance matrix <CODE>x</CODE>. The variable <CODE>var</CODE> contains | |
| 30 the off-subspace variance (which is assumed to be spherical), while the | |
| 31 vector <CODE>lambda</CODE> contains the variances of each of the principal | |
| 32 components. This is computed using the eigenvalue and eigenvector | |
| 33 decomposition of <CODE>x</CODE>. | |
| 34 | |
| 35 <p><h2> | |
| 36 See Also | |
| 37 </h2> | |
| 38 <CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="pca.htm">pca</a></CODE><hr> | |
| 39 <b>Pages:</b> | |
| 40 <a href="index.htm">Index</a> | |
| 41 <hr> | |
| 42 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 43 | |
| 44 | |
| 45 </body> | |
| 46 </html> |