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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>kpcarec.m</title><link rel="stylesheet" type="text/css" href="../../../m-syntax.css"></head><body><code><span class=defun_kw>function</span> <span class=defun_out>Y </span>= <span class=defun_name>kpcarec</span>(<span class=defun_in>X,model</span>)<br><span class=h1>% KPCAREC Reconstructs image after kernel PCA.</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% Y = kpcarec(X,model)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% Input data X are projected using kernel projection trained</span><br><span class=help>% the by Kernel PCA [Mika99b]. The RBF kernel is assumed. This </span><br><span class=help>% function computes the preimages Y from the input space </span><br><span class=help>% corresponding to the projected data are.</span><br><span class=help>%</span><br><span class=help>% X -> projection to -> preimage -> Y</span><br><span class=help>% kernel space problem</span><br><span class=help>% by Kernel PCA</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 vectors.</span><br><span class=help>% model [struct] Kernel projection with RBF kernel;</span><br><span class=help>% see 'help kernelproj'. </span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% Y [dim x num_data] Output data.</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% KPCA, PCAREC.</span><br><span class=help>%</span><br><hr><span class=help1>% <span class=help1_field>About:</span> Statistical Pattern Recognition Toolbox</span><br><span class=help1>% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac</span><br><span class=help1>% <a href="http://www.cvut.cz">Czech Technical University Prague</a></span><br><span class=help1>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a></span><br><span class=help1>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a></span><br><br><span class=help1>% <span class=help1_field>Modifications:</span></span><br><span class=help1>% 17-may-2004, VF</span><br><span class=help1>% 22-apr-2004, VF</span><br><span class=help1>% 17-mar-2004, VF, created.</span><br><br><hr>[dim, num_data] = size(X);<br><br><span class=io>fprintf</span>(<span class=quotes>'Projection data...'</span>);<br>Z = kernelproj(X, model );<br><span class=io>fprintf</span>(<span class=quotes>'done.\n'</span>);<br><br><span class=comment>% allocate memory</span><br>Y = zeros(dim,num_data);<br>img = model;<br><br><span class=io>fprintf</span>(<span class=quotes>'Computing preimages'</span>);<br><span class=keyword>for</span> i=1:num_data,<br> <span class=io>fprintf</span>(<span class=quotes>'.'</span>);<br> <br> img.Alpha = model.Alpha*(Z(:,i) - model.b);<br><br> Y(:,i) = rbfpreimg(img); <span class=comment>% Schoelkopf's fix-point algorithm</span><br><span class=comment>% Y(:,i) = rbfpreimg2(img); % Gradient method</span><br><span class=comment>% Y(:,i) = rbfpreimg3(img,7); % Kwok & Tsang</span><br><br><span class=keyword>end</span><br><span class=io>fprintf</span>(<span class=quotes>'done\n'</span>);<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>?? 快捷鍵說明
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