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gmmb_fracthresh

PURPOSE ^

GMMB_FRACTHRESH Threshold PDF values according to density quantile.

SYNOPSIS ^

function mask = gmmb_fracthresh(pdfmat, histS, thr);

DESCRIPTION ^

GMMB_FRACTHRESH    Threshold PDF values according to density quantile.

     MASK = GMMB_FRACTHRESH(pdfmat, histS, thr)

     pdfmat = N x K matrix of PDF values at N points
              in K different PDFs (the output of gmmb_pdf)
     histS = the histS structure (1 x K cell array) created with
             the bayesS structure that was used to compute PDFs.
     thr = scalar in the range [0, 1], the density quantile

     MASK = N x K logical matrix

     See also GMMB_PDF, GMMB_HIST, GMMB_GENERATEHIST.

  The recommended way to create histS is with gmmb_generatehist.
  The output is a logical N x K matrix that tells whether point N
  is an outlier in distribution K, i.e., it does not belong to the
  thr-quantile of distribution K.

 References:
   [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
    Feature Representation and Discrimination Based on Gaussian Mixture Model
    Probability Densities - Practices and Algorithms, Research Report 95,
    Lappeenranta University of Technology, Department of Information
    Technology, 2005.

 Author(s):
    Pekka Paalanen <pekka.paalanen@lut.fi>

 Copyright:

   Bayesian Classifier with Gaussian Mixture Model Pdf
   functionality is Copyright (C) 2003, 2004 by Pekka Paalanen and
   Joni-Kristian Kamarainen.

   $Name:  $ $Revision: 1.2 $  $Date: 2005/04/14 10:33:34 $

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 %GMMB_FRACTHRESH    Threshold PDF values according to density quantile.
0002 %
0003 %     MASK = GMMB_FRACTHRESH(pdfmat, histS, thr)
0004 %
0005 %     pdfmat = N x K matrix of PDF values at N points
0006 %              in K different PDFs (the output of gmmb_pdf)
0007 %     histS = the histS structure (1 x K cell array) created with
0008 %             the bayesS structure that was used to compute PDFs.
0009 %     thr = scalar in the range [0, 1], the density quantile
0010 %
0011 %     MASK = N x K logical matrix
0012 %
0013 %     See also GMMB_PDF, GMMB_HIST, GMMB_GENERATEHIST.
0014 %
0015 %  The recommended way to create histS is with gmmb_generatehist.
0016 %  The output is a logical N x K matrix that tells whether point N
0017 %  is an outlier in distribution K, i.e., it does not belong to the
0018 %  thr-quantile of distribution K.
0019 %
0020 % References:
0021 %   [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
0022 %    Feature Representation and Discrimination Based on Gaussian Mixture Model
0023 %    Probability Densities - Practices and Algorithms, Research Report 95,
0024 %    Lappeenranta University of Technology, Department of Information
0025 %    Technology, 2005.
0026 %
0027 % Author(s):
0028 %    Pekka Paalanen <pekka.paalanen@lut.fi>
0029 %
0030 % Copyright:
0031 %
0032 %   Bayesian Classifier with Gaussian Mixture Model Pdf
0033 %   functionality is Copyright (C) 2003, 2004 by Pekka Paalanen and
0034 %   Joni-Kristian Kamarainen.
0035 %
0036 %   $Name:  $ $Revision: 1.2 $  $Date: 2005/04/14 10:33:34 $
0037 %
0038 
0039 function mask = gmmb_fracthresh(pdfmat, histS, thr);
0040 
0041 N = size(pdfmat, 1);
0042 K = size(pdfmat, 2);
0043 
0044 thresh = gmmb_frac2lhood(histS, thr*ones(1,K));
0045 mask = (pdfmat < repmat(thresh, N, 1));

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