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ldatrace

PURPOSE ^

LDATRACE Calculates an LDA transform to maximize trace discriminant [a,f,B,W]=(b,w,n,c)

SYNOPSIS ^

function [a,f,B,W]=ldatrace(b,w,n,c)

DESCRIPTION ^

LDATRACE Calculates an LDA transform to maximize trace discriminant [a,f,B,W]=(b,w,n,c)
 If a feature vector X can come from one of several class and W and B are respectively
 the within-class and between-class covariance matrices, then the generalized Fisher discriminant
 F=trace(W\B) is a measure of how well the feature vector discriminates between the classes.
 If we choose a rectangular (tall, skinny) transformation matrix, we can define a smaller
 feature vector Y=A'*X. The aim of this routine is to choose A to maximize the Fisher
 discriminant. We assume that W is positive definite and B is positive semi-definite.
 The input argument C allows the uset to pre-specify some of the columns of A.

 Inputs:
     w[m,m] = within class covariance matrix of x
     b[m,m] = between class covariance matrix of x [default = I]
     n is the number of columns in output matrix A [default = M]
     c[m,r] specifies the first few columns of A to be predefined values [default = null)

 Outputs:
     a[m,n] is the transformation matrix: y=a'*x
     f[n,1] gives the incremental gain in f value for successive columns of A 
     B(n,n) gives the between-class covariance matrix of y
     W[n,n] gives the within-class covariance matrix of y

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function [a,f,B,W]=ldatrace(b,w,n,c)
0002 %LDATRACE Calculates an LDA transform to maximize trace discriminant [a,f,B,W]=(b,w,n,c)
0003 % If a feature vector X can come from one of several class and W and B are respectively
0004 % the within-class and between-class covariance matrices, then the generalized Fisher discriminant
0005 % F=trace(W\B) is a measure of how well the feature vector discriminates between the classes.
0006 % If we choose a rectangular (tall, skinny) transformation matrix, we can define a smaller
0007 % feature vector Y=A'*X. The aim of this routine is to choose A to maximize the Fisher
0008 % discriminant. We assume that W is positive definite and B is positive semi-definite.
0009 % The input argument C allows the uset to pre-specify some of the columns of A.
0010 %
0011 % Inputs:
0012 %     w[m,m] = within class covariance matrix of x
0013 %     b[m,m] = between class covariance matrix of x [default = I]
0014 %     n is the number of columns in output matrix A [default = M]
0015 %     c[m,r] specifies the first few columns of A to be predefined values [default = null)
0016 %
0017 % Outputs:
0018 %     a[m,n] is the transformation matrix: y=a'*x
0019 %     f[n,1] gives the incremental gain in f value for successive columns of A
0020 %     B(n,n) gives the between-class covariance matrix of y
0021 %     W[n,n] gives the within-class covariance matrix of y
0022 
0023 %      Copyright (C) Mike Brookes 1997
0024 %      Version: $Id: ldatrace.m 713 2011-10-16 14:45:43Z dmb $
0025 %
0026 %   VOICEBOX is a MATLAB toolbox for speech processing.
0027 %   Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
0028 %
0029 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0030 %   This program is free software; you can redistribute it and/or modify
0031 %   it under the terms of the GNU General Public License as published by
0032 %   the Free Software Foundation; either version 2 of the License, or
0033 %   (at your option) any later version.
0034 %
0035 %   This program is distributed in the hope that it will be useful,
0036 %   but WITHOUT ANY WARRANTY; without even the implied warranty of
0037 %   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
0038 %   GNU General Public License for more details.
0039 %
0040 %   You can obtain a copy of the GNU General Public License from
0041 %   http://www.gnu.org/copyleft/gpl.html or by writing to
0042 %   Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
0043 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0044 
0045 m=size(b,1);    % dimension of data vectors
0046 if nargin<4
0047     r=0;
0048     if nargin<3
0049         n=m;
0050         if nargin<2
0051             w=eye(m);
0052         end
0053     end
0054 else
0055     r=size(c,2);    % number of columns that are pre-specified
0056 end
0057 if r
0058     if n>r          % need to find additional vectors
0059         g=chol(w);
0060         v=g\null(c'*g');
0061         [p,l,q]=svd(v'*b*v);
0062         a(:,r+1:n)=v*p(:,1:n-r);
0063         a(:,1:r)=c;
0064     else
0065         a=c;        % no new vectors to find
0066     end
0067     if nargout>1
0068         ari=a/triu(qr(chol(a'*w*a))); % matrix a must be of full rank
0069         f=diag(ari'*b*ari);
0070     end
0071 else
0072     [g,d]=eig(b,w,'qz');
0073     [ds,is]=sort(-diag(d));
0074     a=g(:,is(1:n));
0075     if nargout>1
0076         f=-ds(1:n);
0077     end
0078 end
0079 if nargout > 2
0080     B=a'*b*a;
0081     if nargout > 3
0082         W=a'*w*a;
0083     end
0084     
0085 end

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