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<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,Z] = greedykpca(X)</span><br><span class=help> [model,Z] = 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 version of the kernel PCA </span><br><span class=help> algorithm [<a href="../../references.html#Franc03b" title = "" >Franc03b</a>]. The input data X are first approximated by </span><br><span class=help> greedyappx.m and second the ordinary PCA is applyed on the </span><br><span class=help> approximated data. This algorithm has the same objective as </span><br><span class=help> the ordinary Kernel PCA with an extra condition imposed on the </span><br><span class=help> maximal number of data in the resulting kernel projection </span><br><span class=help> (expansion). It greedy KPCA is useful when sparse kernel projection </span><br><span class=help> is desired and when the input data are large.</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 data.</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.</span><br><span class=help> .maxerr [1x1] Desired maximal reconstruction error.</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> .options [struct] Copy of used options.</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> Z [m x num_data] Training data projected by the found kernel projection.</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> XR = kpcarec(X,model); </span><br><span class=help> figure; </span><br><span class=help> ppatterns(X); ppatterns(XR,'+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/greedykpca.html" target="mdsbody">GREEDYKPCA</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> 10-jun-2004, VF<br> 05-may-2004, VF<br> 14-mar-2004, VF<br></body></html>?? 快捷鍵說明
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