📄 greedykpca.html
字号:
<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">GREEDYKPCA</b><td valign="baseline" align="right" class="function"><a href="../../kernels/extraction/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Greedy Kernel Principal Component Analysis.</b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> model = greedykpca(X)</span><br><span class=help> model = greedykpca(X,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function implements a greedy kernel PCA algorithm. </span><br><span class=help> The input data X are first approximated by GREEDYKPCA in the </span><br><span class=help> feature space and second the ordinary PCA is applyed on the </span><br><span class=help> approximated data. This algorithm has the same objective function </span><br><span class=help> as the ordinary Kernel PCA but, in addition, the number of data in </span><br><span class=help> the resulting kernel expansion is limited. </span><br><span class=help></span><br><span class=help> For more info refer to V.Franc: Optimization Algorithms for Kernel </span><br><span class=help> Methods. Research report. CTU-CMP-2005-22. CTU FEL Prague. 2005.</span><br><span class=help> ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-PhD.pdf .</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> X [dim x num_data] Input column vectors.</span><br><span class=help> </span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .ker [string] Kernel identifier. See 'help kernel' for more info.</span><br><span class=help> .arg [1 x narg] Kernel argument.</span><br><span class=help> .m [1x1] Maximal number of base vectors (Default m=0.25*num_data).</span><br><span class=help> .p [1x1] Depth of search for the best basis vector (p=m).</span><br><span class=help> .mserr [1x1] Desired mean squared reconstruction errors of approximation.</span><br><span class=help> .maxerr [1x1] Desired maximal reconstruction error of approximation.</span><br><span class=help> See 'help greedyappx' for more info about the stopping conditions.</span><br><span class=help> .verb [1x1] If 1 then some info is displayed (default 0).</span><br><span class=help> </span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Kernel projection:</span><br><span class=help> .Alpha [nsv x new_dim] Multipliers defining kernel projection.</span><br><span class=help> .b [new_dim x 1] Bias the kernel projection.</span><br><span class=help> .sv.X [dim x num_data] Seleted subset of the training vectors..</span><br><span class=help> .nsv [1x1] Number of basis vectors.</span><br><span class=help> .kercnt [1x1] Number of kernel evaluations.</span><br><span class=help> .MaxErr [1 x nsv] Maximal reconstruction error for corresponding</span><br><span class=help> number of base vectors.</span><br><span class=help> .MsErr [1 x nsv] Mean square reconstruction error for corresponding</span><br><span class=help> number of base vectors.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> X = gencircledata([1;1],5,250,1);</span><br><span class=help> model = greedykpca(X,struct('ker','rbf','arg',4,'new_dim',2));</span><br><span class=help> X_rec = kpcarec(X,model); </span><br><span class=help> figure; </span><br><span class=help> ppatterns(X); ppatterns(X_rec,'+r');</span><br><span class=help> ppatterns(model.sv.X,'ob',12);</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../../kernels/kernelproj.html" target="mdsbody">KERNELPROJ</a>, <a href = "../../kernels/extraction/kpca.html" target="mdsbody">KPCA</a>, <a href = "../../kernels/extraction/greedyappx.html" target="mdsbody">GREEDYAPPX</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../kernels/extraction/list/greedykpca.html">greedykpca.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a><br> <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 09-sep-2005, VF<br> 19-feb-2005, VF<br> 10-jun-2004, VF<br> 05-may-2004, VF<br> 14-mar-2004, VF<br></body></html>⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -