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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">LDA</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>Linear 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 = lda(data)</span><br><span class=help> model = lda(data,new_dim)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function is implementation of Linear Discriminant Analysis.</span><br><span class=help> The goal is to train the linear transform which maximizes ratio </span><br><span class=help> between between-class and within-class scatter matrix of projected </span><br><span class=help> data.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Input labeled data:</span><br><span class=help> .X [dim x num_data] Data sample.</span><br><span class=help> .y [1 x num_data] Labels (1,2,...,nclass).</span><br><span class=help></span><br><span class=help> new_dim [1x1] Output data dimension (default new_dim = dim).</span><br><span class=help></span><br><span class=help> <span class=help_field>Ouput:</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] Biases.</span><br><span class=help></span><br><span class=help> .mean_X [dim x 1] Mean value of data.</span><br><span class=help> .Sw [dim x dim] Within-class scatter matrix.</span><br><span class=help> .Sb [dim x dim] Between-class scatter matrix.</span><br><span class=help> .eigval [dim x 1] Eigenvalues.</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 = lda( in_data, 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/pca.html" target="mdsbody">PCA</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/extraction/list/lda.html">lda.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> 25-may-2004, VF<br> 3-may-2004, VF<br> 20-may-2001, V.Franc, created<br></body></html>⌨️ 快捷键说明
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