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Add option to standardize data before obtaining shrinkage gamma #216

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Add option to standardize data before obtaining shrinkage gamma #216

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16 changes: 13 additions & 3 deletions classification/utils/clsutil_shrinkage.m
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,9 @@
% 'C' (common covariance)
% 'D' (diagonal, unequal variance)
% 'Gamma': DOUBLE - Shrinkage parameter. May be used to set the shrinkage parameter explicitly.
% 'Standardize': BOOL (default 0) - If set, standardizes dimensions before calculating optimal
% Shrinkage gamma. The obtained covariance matrix is scaled back into the
% original feature range.
% 'Verbose': BOOL (default 0) - If set, verbose mode is activated
%
%Returns:
Expand Down Expand Up @@ -44,9 +47,10 @@
% opt-type checking (Michael Tangermann)


props= {'Target' 'B' 'CHAR(A B C D)'
'Gamma' 'auto' 'CHAR(auto)|!DOUBLE'
'Verbose' 0 'BOOL'};
props= {'Target' 'B' 'CHAR(A B C D)'
'Gamma' 'auto' 'CHAR(auto)|!DOUBLE'
'Standardize' 0 'BOOL'
'Verbose' 0 'BOOL'};

if nargin==0,
Cstar= props;
Expand All @@ -66,6 +70,9 @@

%%% Empirical covariance
[p, n] = size(X);
if opt.Standardize
[X, ~, z_sigma] = zscore(X, 0, 2);
end
Xn = X - repmat(mean(X,2), [1 n]);
S = Xn*Xn';
Xn2 = Xn.^2;
Expand Down Expand Up @@ -112,3 +119,6 @@

%%% Estimate covariance matrix
Cstar = (gamma*T + (1-gamma)*S ) / (n-1);
if opt.Standardize
Cstar = (z_sigma * z_sigma') .* Cstar;
end