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<title>RankNCG_linear_train</title>
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<h1>RankNCG_linear_train</h1>
<introduction>
<p>Trains a linear ranking function using nonlinear conjugate gradient.</p>
</introduction>
<h2>Contents</h2>
<div>
<ul>
<li><a href="#1">Syntax</a></li>
<li><a href="#2">Description</a></li>
<li><a href="#6">Input</a></li>
<li><a href="#14">Ouput</a></li>
<li><a href="#16">Signature</a></li>
<li><a href="#18">See also</a></li>
</ul>
</div>
<h2>Syntax<a name="1"></a></h2><pre>[w,time_taken]=RankNCG_linear_train(data,CG_tolerance,lambda,method,epsil,verbose,plot_enable)</pre><h2>Description<a name="2"></a></h2>
<p>The exct version scales as <img src="equations/script_RankNCG_linear_train_eq966.png" align="middle">.</p>
<p>The approximate version scales as <img src="equations/script_RankNCG_linear_train_eq622.png" align="middle">.</p>
<p>CG_tolerance and lambda can be chosen by cross-validation.</p>
<h2>Input<a name="6"></a></h2>
<div>
<ul>
<li>data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]</li>
<li>CG_tolerance ... tolerance for the conjugate gradient procedure [Set to 1e-3 to 1e-6]</li>
<li>lambda ... regularization parameter.</li>
<li>method ... if 'direct' uses the exact gradient, if 'approx' uses the approximate gradient</li>
<li>epsil ... the accuracy parameter for the fast approximate computation of erfc functions [Set to 1e-3 to 1e-6]</li>
<li>verbose ... if 1 comments are displayed. Set to 0 when timing the program</li>
<li>plot_enable ... if 1 plots the learning curve</li>
</ul>
</div>
<p>Basic information---</p>
<div>
<ul>
<li>data.N ... number of data points</li>
<li>data.d ... data dimensionality</li>
<li>data.S ... number of classes</li>
<li>data.m ... number of inputs in each class</li>
</ul>
</div>
<p>Actual data---</p>
<div>
<ul>
<li>data.labels ... vector of class labels</li>
<li>data.X ... cell array where each cell contains the data belonging to one class</li>
<li>data.index ... index of the data belonging to one class</li>
<li>data.X_raw ... d x N original data matrix</li>
<li>data.y_raw ... 1 x N vector of the class labels</li>
</ul>
</div>
<p>Preference graph---</p>
<div>
<ul>
<li>data.graph_type ... graph type</li>
<li>data.C ... number of edges in the preference graph</li>
<li>data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the
first column.
</li>
<li>data.num_of_pairs ... total number of pairwise preference realtions</li>
</ul>
</div>
<h2>Ouput<a name="14"></a></h2>
<div>
<ul>
<li> w ... d x 1 learnt weight vector</li>
<li> time_taken ... in seconds</li>
</ul>
</div>
<h2>Signature<a name="16"></a></h2>
<div>
<ul>
<li><b>Author:</b> Vikas Chandrakant Raykar
</li>
<li><b>E-Mail:</b> <a href="mailto:vikas@cs.umd.edu">vikas@cs.umd.edu</a> Date: September 27, 2006
</li>
</ul>
</div>
<h2>See also<a name="18"></a></h2>
<p><a href="convert_data_to_ranking_format.html">convert_data_to_ranking_format</a>, <a href="non_linear_conjugate_gradient.html">non_linear_conjugate_gradient</a></p>
<p class="footer"><br>
Published with wg_publish; V1.0<br></p>
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<!--
##### SOURCE BEGIN #####
%% RankNCG_linear_train
% Trains a linear ranking function using nonlinear conjugate gradient.
%% Syntax
% [w,time_taken]=RankNCG_linear_train(data,CG_tolerance,lambda,method,epsil,verbose,plot_enable)
%% Description
%%
% The exct version scales as <img src="equations/script_RankNCG_linear_train_eq966.png" align="middle">.
%%
% The approximate version scales as <img src="equations/script_RankNCG_linear_train_eq622.png" align="middle">.
%%
% CG_tolerance and lambda can be chosen by cross-validation.
%%
%% Input
%%
%%
% * data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]
% * CG_tolerance ... tolerance for the conjugate gradient procedure [Set to 1e-3 to 1e-6]
% * lambda ... regularization parameter.
% * method ... if 'direct' uses the exact gradient, if 'approx' uses the approximate gradient
% * epsil ... the accuracy parameter for the fast approximate computation of erfc functions [Set to 1e-3 to 1e-6]
% * verbose ... if 1 comments are displayed. Set to 0 when timing the program
% * plot_enable ... if 1 plots the learning curve
%%
% Basic informationREPLACE_WITH_DASH_DASH-
%%
%%
% * data.N ... number of data points
% * data.d ... data dimensionality
% * data.S ... number of classes
% * data.m ... number of inputs in each class
%%
% Actual dataREPLACE_WITH_DASH_DASH-
%%
%%
% * data.labels ... vector of class labels
% * data.X ... cell array where each cell contains the data belonging to one class
% * data.index ... index of the data belonging to one class
% * data.X_raw ... d x N original data matrix
% * data.y_raw ... 1 x N vector of the class labels
%%
% Preference graphREPLACE_WITH_DASH_DASH-
%%
%%
% * data.graph_type ... graph type
% * data.C ... number of edges in the preference graph
% * data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the first column.
% * data.num_of_pairs ... total number of pairwise preference realtions
%%
%% Ouput
%%
%%
% * w ... d x 1 learnt weight vector
% * time_taken ... in seconds
%%
%% Signature
%%
%%
% * *Author:* Vikas Chandrakant Raykar
% * *E-Mail:* vikas@cs.umd.edu
% Date: September 27, 2006
%%
%% See also
%%
% convert_data_to_ranking_format, non_linear_conjugate_gradient
%%
%%
%
##### SOURCE END #####
-->
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