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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">GDA</b><td valign="baseline" align="right" class="function"><a href="../../kernels/extraction/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Generalized Discriminant 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 = gda(data)
</span><br><span class=help> model = gda(data,options)
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function is implimentation of the Generalized Discriminant
</span><br><span class=help> Analysis (GDA) [<a href="../../references.html#Baudat01" title = "G.Baudat and F.Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10):2385--2404, 2000. citeseer.nj.nec.com/baudat00generalized.html." >Baudat01</a>]. The GDA is kernelized version of
</span><br><span class=help> the Linear Discriminant Analysis (LDA). It produce the kernel data
</span><br><span class=help> projection which increases class separability of the projected
</span><br><span class=help> training data.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Labeled training data:
</span><br><span class=help> .X [dim x num_data] Training vectors.
</span><br><span class=help> .y [1 x num_data] Labels (1,2,..,mclass).
</span><br><span class=help>
</span><br><span class=help> options [struct] Defines kernel and a output dimension:
</span><br><span class=help> .ker [string] Kernel identifier (default 'linear');
</span><br><span class=help> see 'help kernel' for more info.
</span><br><span class=help> .arg [1 x nargs] Kernel arguments (default 1).
</span><br><span class=help> .new_dim [1x1] Output dimension (default dim).
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Kernel projection:
</span><br><span class=help> .Alpha [num_data x new_dim] Multipliers.
</span><br><span class=help> .b [new_dim x 1] Bias.
</span><br><span class=help> .sv.X [dim x num_data] Training data.
</span><br><span class=help> .options [struct] Copy of used options.
</span><br><span class=help> .rankK [int] Rank of centered kernel matrix.
</span><br><span class=help> .nsv [int] Number 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 = gda(in_data,struct('ker','rbf','arg',1));
</span><br><span class=help> out_data = kernelproj( 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 = "../../kernels/kernelproj.html" target="mdsbody">KERNELPROJ</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= "../../kernels/extraction/list/gda.html">gda.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> 24-may-2004, VF
<br> 4-may-2004, VF
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