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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">PCA</b><td valign="baseline" align="right" class="function"><a href="../../linear/extraction/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table>  <p><b>Principal Component Analysis.</b></p>  <hr><div class='code'><code><span class=help></span><br><span class=help>&nbsp;<span class=help_field>Synopsis:</span></span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(X)</span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(X,new_dim)</span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(X,var)</span><br><span class=help></span><br><span class=help>&nbsp;<span class=help_field>Description:</span></span><br><span class=help>&nbsp;&nbsp;It&nbsp;computes&nbsp;Principal&nbsp;Component&nbsp;Analysis,&nbsp;i.e.,&nbsp;the</span><br><span class=help>&nbsp;&nbsp;linear&nbsp;transform&nbsp;which&nbsp;makes&nbsp;data&nbsp;uncorrelated&nbsp;and</span><br><span class=help>&nbsp;&nbsp;minize&nbsp;the&nbsp;reconstruction&nbsp;error.</span><br><span class=help></span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(X,new_dim)&nbsp;use&nbsp;to&nbsp;specify&nbsp;explicitely&nbsp;output</span><br><span class=help>&nbsp;&nbsp;&nbsp;dimesnion&nbsp;where&nbsp;new_dim&nbsp;&gt;=&nbsp;1.</span><br><span class=help></span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(X,var)&nbsp;use&nbsp;to&nbsp;specify&nbsp;a&nbsp;portion&nbsp;of&nbsp;discarded</span><br><span class=help>&nbsp;&nbsp;&nbsp;variance&nbsp;in&nbsp;data&nbsp;where&nbsp;0&nbsp;&lt;=&nbsp;var&nbsp;&lt;&nbsp;1.&nbsp;The&nbsp;new_dim&nbsp;is&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;selected&nbsp;be&nbsp;as&nbsp;small&nbsp;as&nbsp;possbile&nbsp;and&nbsp;to&nbsp;satisfy&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;var&nbsp;&gt;=&nbsp;MsErr(new_dim)/MaxMsErr&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;where&nbsp;MaxMsErr&nbsp;=&nbsp;sum(sum(X.^2)).&nbsp;</span><br><span class=help></span><br><span class=help>&nbsp;<span class=help_field>Input:</span></span><br><span class=help>&nbsp;&nbsp;X&nbsp;[dim&nbsp;x&nbsp;num_data]&nbsp;training&nbsp;data&nbsp;stored&nbsp;as&nbsp;columns.</span><br><span class=help></span><br><span class=help>&nbsp;&nbsp;new_dim&nbsp;[1x1]&nbsp;Output&nbsp;dimension;&nbsp;new_dim&nbsp;&gt;&nbsp;1&nbsp;(default&nbsp;new_dim&nbsp;=&nbsp;dim);</span><br><span class=help>&nbsp;&nbsp;var&nbsp;[1x1]&nbsp;Portion&nbsp;of&nbsp;discarded&nbsp;variance&nbsp;in&nbsp;data.</span><br><span class=help></span><br><span class=help>&nbsp;<span class=help_field>Ouputs:</span></span><br><span class=help>&nbsp;&nbsp;model&nbsp;[struct]&nbsp;Linear&nbsp;projection:</span><br><span class=help>&nbsp;&nbsp;&nbsp;.W&nbsp;[dim&nbsp;x&nbsp;new_dim]&nbsp;Projection&nbsp;matrix.</span><br><span class=help>&nbsp;&nbsp;&nbsp;.b&nbsp;[new_dim&nbsp;x&nbsp;1]&nbsp;Bias.</span><br><span class=help>&nbsp;&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;.eigval&nbsp;[dim&nbsp;x&nbsp;1]&nbsp;eigenvalues.</span><br><span class=help>&nbsp;&nbsp;&nbsp;.mse&nbsp;[real]&nbsp;Mean&nbsp;square&nbsp;representation&nbsp;error.</span><br><span class=help>&nbsp;&nbsp;&nbsp;.MsErr&nbsp;[dim&nbsp;x&nbsp;1]&nbsp;Mean-square&nbsp;errors&nbsp;with&nbsp;respect&nbsp;to&nbsp;number&nbsp;</span><br><span class=help>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;basis&nbsp;vectors;&nbsp;mse=MsErr(new_dim).</span><br><span class=help>&nbsp;&nbsp;&nbsp;.mean_X&nbsp;[dim&nbsp;x&nbsp;1]&nbsp;mean&nbsp;of&nbsp;training&nbsp;data.</span><br><span class=help></span><br><span class=help>&nbsp;<span class=help_field>Example:</span></span><br><span class=help>&nbsp;&nbsp;in_data&nbsp;=&nbsp;load('iris');</span><br><span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;pca(in_data.X,&nbsp;2)</span><br><span class=help>&nbsp;&nbsp;out_data&nbsp;=&nbsp;linproj(in_data,model);</span><br><span class=help>&nbsp;&nbsp;figure;&nbsp;ppatterns(out_data);</span><br><span class=help></span><br><span class=help>&nbsp;<span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also>&nbsp;&nbsp;<a href = "../../linear/linproj.html" target="mdsbody">LINPROJ</a>,&nbsp;<a href = "../../linear/extraction/pcarec.html" target="mdsbody">PCAREC</a>,&nbsp;<a href = "../../kernels/extraction/kpca.html" target="mdsbody">KPCA</a>.</span><br><span class=help></span><br></code></div>  <hr>  <b>Source:</b> <a href= "../../linear/extraction/list/pca.html">pca.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> 20-may-2004, VF<br> 20-june-2003, VF<br> 21-jan-03, VF<br> 20-jan-03, VF<br> 16-Jan-2003, VF, new comments.<br> 26-jun-2002, VF<br></body></html>

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