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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">TRAIN_LIN_DENOIS</b><td valign="baseline" align="right" class="function"><a href="../../demos/image_denoising/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Training of linear PCA model for image denoising. </b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> The linear PCA model is trained to describe an input</span><br><span class=help> class of images corrupted by noise. The training data </span><br><span class=help> contains images corrupted by noise and corresponding </span><br><span class=help> ground truth. The output dimension of the linear PCA</span><br><span class=help> is tuned by cross-validation. The objective function </span><br><span class=help> is a sum of squared differences between ground truth </span><br><span class=help> images and reconstructed images. </span><br><span class=help></span><br><span class=help> See also</span><br><span class=help> PCA, PCAREC, LINPROJ.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../demos/image_denoising/list/train_lin_denois.html">train_lin_denois.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> 07-jun-2004, VF<br> 05-may-2004, VF<br> 17-mar-2004, VF<br></body></html>⌨️ 快捷键说明
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