pca_dd.m

模式识别工具箱,希望对大家有用!

%PCA_DD Principal Component data description
%
%       W = pca_dd(A,fracrej,n)
%
% Traininig of a PCA, with n features (or explaining a fraction n of
% the variance).
% Default: n=0.9

% Copyright: D. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands

function W = pca_dd(a,fracrej,n)

if (nargin<3)
  n = 0.9;
end
if (nargin<2)
  fracrej = 0.05;
end
if (nargin<1)|isempty(a)
  W = mapping(mfilename,{fracrej,n});
  return
end

if isa(fracrej,'double')           %training

  if ~isa(a,'dataset')              %train on training set
    error('PCA_DD: I need a dataset to train');
  end
  % remove the labels:
  a = target_class(a);     % only use the target class
  [nlab,lablist,m,k,c,prob,featlist] = dataset(a);
  % be careful with the mean:
  meana = ones(m,1)*mean(a);
  a = +(a - meana);

  % train it and compute the reconstruction error:
  if exist('pca','file')
    w = pca(a, n);
  else
    w = klm(a, n);
  end
  W = +w;
  if size(W,2)==k
    warning('PCA_DD: Output dimensionality is equal to input dimensionality!');
  end
  W(end,:) = [];
  Proj = W*inv(W'*W)*W';
  dif = a - a*Proj;
  d = sum(dif.*dif,2);
  
  % obtain the threshold:
  thr = -threshold(-d,fracrej);

  %and save all useful data:
  % (I know I just have to store W instead of Proj, but I do not like
  % to compute the inverse of W'*W over and over again, this uses just
  % some disk and memory space):
  W.P = Proj;
  W.m = meana(1,:);
  W.threshold = thr;
  W.scale = mean(d);
  W = mapping(mfilename,W,str2mat('target','outlier'),k,c);

else                               %testing

  [W,classlist,type,k,c] = mapping(fracrej);  % unpack
  [nlab,lablist,m,k,c,p] = dataset(a);

  %compute reconstruction error:
  dif = +a - ones(m,1)*W.m;
  dif = dif - dif*W.P;
  out = sum(dif.*dif,2);
  out = [out, ones(m,1)*W.threshold];

  % map to probabilities:
  newout = dist2dens(out,W.scale);

  W = dataset(newout,getlab(a),classlist,p,lablist);

end

return