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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">RSDE</b><td valign="baseline" align="right" class="function"><a href="../../probab/estimation/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Reduced Set Density Estimator.</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 = rsde(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 the Reduced Set Density Estimator </span><br><span class=help> [<a href="../../references.html#Girol03" title = "" >Girol03</a>] which provides kernel density estimate optimal </span><br><span class=help> in the L2 sense. The density is modeled as the weighted sum </span><br><span class=help> of Gaussians (RBF kernel) centered in selected subset of </span><br><span class=help> training data. </span><br><span class=help></span><br><span class=help> The estimation is expressed as a special instance of the</span><br><span class=help> Quadratic Programming task (see 'help gmnp').</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 sample.</span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .arg [1x1] Standard deviation of the Gaussian kernel.</span><br><span class=help> .solver [string] QP solver (see 'help gmnp'); 'imdm' default.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Output density model:</span><br><span class=help> .Alpha [nsv x 1] Weights of the kernel functions.</span><br><span class=help> .sv.X [dim x nsv] Selected centers of kernel functions.</span><br><span class=help> .nsv [1x1] Number of selected centers.</span><br><span class=help> .options.arg = options.arg.</span><br><span class=help> .options.ker = 'rbf'</span><br><span class=help> .stat [struct] Statistics about optimization:</span><br><span class=help> .access [1x1] Number of requested columns of matrix H.</span><br><span class=help> .t [1x1] Number of iterations.</span><br><span class=help> .UB [1x1] Upper bound on the optimal value of criterion. </span><br><span class=help> .LB [1x1] Lower bound on the optimal value of criterion. </span><br><span class=help> .LB_History [1x(t+1)] LB with respect to iteration.</span><br><span class=help> .UB_History [1x(t+1)] UB with respect to iteration.</span><br><span class=help> .NA [1x1] Number of non-zero entries in solution.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> gnd = struct('Mean',[-2 3],'Cov',[1 0.5],'Prior',[0.4 0.6]);</span><br><span class=help> sample = gmmsamp( gnd, 1000 );</span><br><span class=help> figure; hold on; ppatterns(sample.X);</span><br><span class=help> plot([-4:0.1:8], pdfgmm([-4:0.1:8],gnd),'r');</span><br><span class=help></span><br><span class=help> model = rsde(sample.X,struct('arg',0.7));</span><br><span class=help> x = linspace(-4,8,100);</span><br><span class=help> plot(x,kernelproj(x,model),'g'); </span><br><span class=help> ppatterns(model.sv.X,'ob',13);</span><br><span class=help> Reduction = model.nsv/size(sample.X,2)</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 = "../../probab/estimation/emgmm.html" target="mdsbody">EMGMM</a>, <a href = "../../probab/estimation/mlcgmm.html" target="mdsbody">MLCGMM</a>, GMNP.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../probab/estimation/list/rsde.html">rsde.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2005, 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> 24-jan-2005, VF, Fast QP solver (GMNP) was used instead of QUADPROG.<br> 17-sep-2004, VF, revised<br></body></html>?? 快捷鍵說明
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