📄 kernelproj.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">KERNELPROJ</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>Kernel projection.
</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> Y = kernelproj(X, model)
</span><br><span class=help> out_data = kernelproj(in_data, model)
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> Y = kernelproj(X, model) this function maps input vectors
</span><br><span class=help> X [dim x num_data] onto vectors Y [new_dim x num_data]
</span><br><span class=help> using the kernel projection
</span><br><span class=help>
</span><br><span class=help> Y(:,i) = Alpha' * kernel(sv.X, X(:,i), ker, arg) + b
</span><br><span class=help>
</span><br><span class=help> where parameters of the projection are given in model:
</span><br><span class=help> .Alpha [nsv x new_dim] Multipliers.
</span><br><span class=help> .b [new_dim x 1] Bias.
</span><br><span class=help> .sv.X [dim x nsv] Vectors.
</span><br><span class=help> .options.ker [string] Kernel identifier.
</span><br><span class=help> .options.arg [1 x narg] Kernel argument.
</span><br><span class=help>
</span><br><span class=help> out_data = kernelproj(in_data, model) assumes that in_data
</span><br><span class=help> is a structure containing vectors X and labels y.
</span><br><span class=help> The output structute out_data is constructed as
</span><br><span class=help>
</span><br><span class=help> out_data.X = kernelproj(in_data.X, model)
</span><br><span class=help> out_data.y = in_data.y
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> help kpca;
</span><br><span class=help> help gda;
</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/extraction/gda.html" target="mdsbody">GDA</a>, <a href = "../kernels/extraction/kpca.html" target="mdsbody">KPCA</a>, <a href = "../linear/linproj.html" target="mdsbody">LINPROJ</a>, <a href = "../kernels/kernel.html" target="mdsbody">KERNEL</a>.
</span><br><span class=help>
</span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/kernelproj.html">kernelproj.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> 19-sep-2004, VF, core of the function rewritten to C
<br> 14-may-2004, VF
<br> 4-may-2004, VF
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