Description of sl2dpca_apply

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sl2dpca_apply

PURPOSE

SL2DPCA_APPLY Applies 2D PCA onto a set of matrices to extract features

SYNOPSIS

function Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)

DESCRIPTION

SL2DPCA_APPLY Applies 2D PCA onto a set of matrices to extract features

 $ Syntax $
   - Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)

 $ Description $
   - Mm:           the mean matrix
   - PL:           the left projection matrix
   - PR:           the right projection matrix
   - data:         the matrix samples or the cell array of filenames
   - matsiz:       the original matrix size
   - n:            the number of samples
   - Y:            the extracted 2D features

 $ Description $
   - Y = sl2dpca_apply(data, Mm, PL, PR) extracts 2D features for 
     the matrices given in data, in either a 3D array or a set of
     array filenames. Suppose the original matrix size is d1 x d2,
     PL be d1 x k1, PR be d2 x k2, then the feature matrix would be
     of size k1 x k2. Y is a k1 x k2 x n array.

 $ History $
   - Created by Dahua Lin, on Jul 31st, 2006

CROSS-REFERENCE INFORMATION

This function calls:

• slreadarray SLREADARRAY Reads an array from an array file
• raise_lackinput RAISE_LACKINPUT Raises an error indicating lack of input argument

This function is called by:

SUBFUNCTIONS

• function Y = computeY(X, Mm, PL, PR)

SOURCE CODE

0001 function Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)
0002 %SL2DPCA_APPLY Applies 2D PCA onto a set of matrices to extract features
0003 %
0004 % $ Syntax $
0005 %   - Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)
0006 %
0007 % $ Description $
0008 %   - Mm:           the mean matrix
0009 %   - PL:           the left projection matrix
0010 %   - PR:           the right projection matrix
0011 %   - data:         the matrix samples or the cell array of filenames
0012 %   - matsiz:       the original matrix size
0013 %   - n:            the number of samples
0014 %   - Y:            the extracted 2D features
0015 %
0016 % $ Description $
0017 %   - Y = sl2dpca_apply(data, Mm, PL, PR) extracts 2D features for
0018 %     the matrices given in data, in either a 3D array or a set of
0019 %     array filenames. Suppose the original matrix size is d1 x d2,
0020 %     PL be d1 x k1, PR be d2 x k2, then the feature matrix would be
0021 %     of size k1 x k2. Y is a k1 x k2 x n array.
0022 %
0023 % $ History $
0024 %   - Created by Dahua Lin, on Jul 31st, 2006
0025 %
0026 
0027 %% Parse and verify input arguments
0028 
0029 if nargin < 6
0030     raise_lackinput('sl2dpca_apply', 6);
0031 end
0032 
0033 matsiz = matsiz(:)';
0034 if length(matsiz) ~= 2
0035     error('sltoolbox:invalidarg', ...
0036         'matsiz should be a 2-elem vector');
0037 end
0038 
0039 if ~isequal(size(Mm), matsiz)
0040     error('sltoolbox:sizmismatch', ...
0041         'the sample size does not match the model');
0042 end
0043 d1 = matsiz(1);
0044 d2 = matsiz(2);
0045 
0046 if size(PL, 1) ~= d1 || size(PR, 1) ~= d2
0047     error('sltoolbox:sizmismatch', ...
0048         'the size of projection matrices are illegal');
0049 end
0050 
0051 %% Compute
0052 
0053 if isnumeric(data)
0054     
0055     if size(data, 3) ~= n
0056         error('sltoolbox:sizmismatch', ...
0057             'The number of samples is not as specified');
0058     end
0059     
0060     Y = computeY(data, Mm, PL, PR);
0061     
0062 elseif iscell(data)
0063     
0064     Y = zeros(size(PL, 2), size(PR, 2), n);
0065     
0066     nfiles = length(data);
0067     cf = 0;
0068     for i = 1 : nfiles
0069         curdata = slreadarray(data{i});
0070         curn = size(curdata, 3);
0071         Y(:,:,cf+1:cf+curn) = computeY(curdata, Mm, PL, PR);
0072         cf = cf + curn;
0073     end
0074     
0075 else
0076     error('sltoolbox:invalidarg', ...
0077         'data should be a numeric array or a cell array of filenames');    
0078  
0079 end
0080 
0081 
0082 %% Core compute function
0083 
0084 function Y = computeY(X, Mm, PL, PR)
0085 
0086 n = size(X, 3);
0087 Y = zeros(size(PL, 2), size(PR, 2), n);
0088 PLT = PL';
0089 
0090 for i = 1 : n
0091     Y(:,:,i) = PLT * (X(:,:,i) - Mm) * PR;
0092 end
0093 
0094 
0095 
0096 
0097 
0098     
0099 
0100 
0101 
0102

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