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first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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> |