Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/nethelp3.3/pca.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
|---|---|
| date | Tue, 10 Feb 2015 15:05:51 +0000 |
| parents | |
| children |
comparison
equal
deleted
inserted
replaced
| -1:000000000000 | 0:e9a9cd732c1e |
|---|---|
| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual pca | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> pca | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Principal Components Analysis | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 PCcoeff = pca(data) | |
| 20 PCcoeff = pca(data, N) | |
| 21 [PCcoeff, PCvec] = pca(data) | |
| 22 </PRE> | |
| 23 | |
| 24 | |
| 25 <p><h2> | |
| 26 Description | |
| 27 </h2> | |
| 28 | |
| 29 <CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariance | |
| 30 matrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>. These | |
| 31 coefficients give the variance of <CODE>data</CODE> along the corresponding | |
| 32 principal components. | |
| 33 | |
| 34 <p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues. | |
| 35 | |
| 36 <p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components as | |
| 37 well as the coefficients. This is considerably more computationally | |
| 38 demanding than just computing the eigenvalues. | |
| 39 | |
| 40 <p><h2> | |
| 41 See Also | |
| 42 </h2> | |
| 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> | |
| 44 <b>Pages:</b> | |
| 45 <a href="index.htm">Index</a> | |
| 46 <hr> | |
| 47 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 48 | |
| 49 | |
| 50 </body> | |
| 51 </html> |