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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> <span class=help_field>Synopsis:</span></span><br><span class=help> model = pca(X)</span><br><span class=help> model = pca(X,new_dim)</span><br><span class=help> model = pca(X,var)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It computes Principal Component Analysis, i.e., the</span><br><span class=help> linear transform which makes data uncorrelated and</span><br><span class=help> minize the reconstruction error.</span><br><span class=help></span><br><span class=help> model = pca(X,new_dim) use to specify explicitely output</span><br><span class=help> dimesnion where new_dim >= 1.</span><br><span class=help></span><br><span class=help> model = pca(X,var) use to specify a portion of discarded</span><br><span class=help> variance in data where 0 <= var < 1. The new_dim is </span><br><span class=help> selected be as small as possbile and to satisfy </span><br><span class=help> var >= MsErr(new_dim)/MaxMsErr </span><br><span class=help> </span><br><span class=help> where MaxMsErr = sum(sum(X.^2)). </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] training data stored as columns.</span><br><span class=help></span><br><span class=help> new_dim [1x1] Output dimension; new_dim > 1 (default new_dim = dim);</span><br><span class=help> var [1x1] Portion of discarded variance in data.</span><br><span class=help></span><br><span class=help> <span class=help_field>Ouputs:</span></span><br><span class=help> model [struct] Linear projection:</span><br><span class=help> .W [dim x new_dim] Projection matrix.</span><br><span class=help> .b [new_dim x 1] Bias.</span><br><span class=help> </span><br><span class=help> .eigval [dim x 1] eigenvalues.</span><br><span class=help> .mse [real] Mean square representation error.</span><br><span class=help> .MsErr [dim x 1] Mean-square errors with respect to number </span><br><span class=help> of basis vectors; mse=MsErr(new_dim).</span><br><span class=help> .mean_X [dim x 1] mean of training data.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> in_data = load('iris');</span><br><span class=help> model = pca(in_data.X, 2)</span><br><span class=help> out_data = linproj(in_data,model);</span><br><span class=help> figure; ppatterns(out_data);</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 = "../../linear/linproj.html" target="mdsbody">LINPROJ</a>, <a href = "../../linear/extraction/pcarec.html" target="mdsbody">PCAREC</a>, <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>?? 快捷鍵說(shuō)明
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