📄 dualcov.html
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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">DUALCOV</b><td valign="baseline" align="right" class="function"><a href="../kernels/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Dual representation of covariance matrix.</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> Z=dualcov(num_data)</span><br><span class=help> Z=dualcov(labels, y)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function computes a matrix Z [num_data x num_data] which allows </span><br><span class=help> to express the sample covariance matrix of data sample X [dim x num_data] </span><br><span class=help> in terms of dot products. </span><br><span class=help></span><br><span class=help> Z = dualcov(num_data) computes a matrix Z [num_data x num_data] such that </span><br><span class=help> cov(X',1) = X*Z*X'.</span><br><span class=help></span><br><span class=help> m = dualcov(labels,y) computes a matrix Z [length(y) x length(y)] such that</span><br><span class=help> cov(X(:,find(labels==y))',1) = X*Z*X',</span><br><span class=help></span><br><span class=help> where labels [1 x num_data] is a vector of data labels and y [1x1] </span><br><span class=help> is a label od class which covariance metrix is to be computed.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> Unlabeled data:</span><br><span class=help> data = load('riply_trn');</span><br><span class=help> ca = cov( data.X', 1)</span><br><span class=help> cb = data.X*dualcov(size(data.X,2))*data.X'</span><br><span class=help></span><br><span class=help> Labeled data:</span><br><span class=help> data = load('riply_trn');</span><br><span class=help> ca1 = cov( data.X(:,find(data.y==1))',1)</span><br><span class=help> cb1 = data.X*dualcov(data.y,1)*data.X'</span><br><span class=help> ca2 = cov( data.X(:,find(data.y==2))',1)</span><br><span class=help> cb2 = data.X*dualcov(data.y,2)*data.X'</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/dualmean.html" target="mdsbody">DUALMEAN</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/dualcov.html">dualcov.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> 16-may-2004, VF<br> 14-may-2004, VF<br> 22-Jan-2003, VF<br> 22-May-2001, V. Franc, created<br></body></html>⌨️ 快捷键说明
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