You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: SDA.m
    path of file: ./repos/MatlabFunc/SubspaceLearning
    
    the code of the file until where you have to start completion: function [eigvector, eigvalue] = SDA(gnd,fea,semiSplit,options)
% SDA: Semi-supervised Discriminant Analysis
%
%       [eigvector, eigvalue] = SDA(gnd,feaLabel,feaUnlabel,options)
% 
%             Input:
%               gnd     - Label vector.  
%               fea     - data matrix. Each row vector of fea is a data point. 

    
function [eigvector, eigvalue] = SDA(gnd,fea,semiSplit,options)
% SDA: Semi-supervised Discriminant Analysis
%
%       [eigvector, eigvalue] = SDA(gnd,feaLabel,feaUnlabel,options)
% 
%             Input:
%               gnd     - Label vector.  
%               fea     - data matrix. Each row vector of fea is a data point. 
%
%             semiSplit - fea(semiSplit,:) is the labeled data matrix. 
%                       - fea(~semiSplit,:) is the unlabeled data matrix. 
%
%               options - Struct value in Matlab. The fields in options
%                         that can be set:
%
%                     WOptions     Please see ConstructW.m for detailed options.  
%                       or
%                      W           You can construct the W outside.
%
%                     ReguBeta     Paramter to tune the weight between
%                                  supervised info and local info
%                                    Default 0.1. 
%                                       beta*L+\tilde{I} 
%                     ReguAlpha    Paramter of Tinkhonov regularizer
%                                    Default 0.1. 
%
%                         Please see LGE.m for other options.
%
%             Output:
%               eigvector - Each column is an embedding function, for a new
%                           data point (row vector) x,  y = x*eigvector
%                           will be the embedding result of x.
%               eigvalue  - The eigvalue of SDA eigen-problem. sorted from
%                           smallest to largest. 
%
%    Examples:
%
%       
% 
%
% See also LPP, LGE
%
%Reference:
%
%   Deng Cai, Xiaofei He and Jiawei Han, "Semi-Supervised Discriminant
%   Analysis ", IEEE International Conference on Computer Vision (ICCV),
%   Rio de Janeiro, Brazil, Oct. 2007.   
%
%   version 2.0 --July/2007 
%   version 1.0 --May/2006 
%
%   Written by Deng Cai (dengcai2 AT cs.uiuc.edu)



if ~isfield(options,'ReguType')
    options.ReguType = 'Ridge';
end