| [5] | 1 | %EMPARZENC EM-algorithm for semi-supervised learning by parzenc
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| 2 | %
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| 3 | % [W,V] = EMPARZENC(A,B,N,FID)
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| 4 | % W = A*EMPARZENC([],B,N,FID)
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| 5 | %
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| 6 | % INPUT
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| 7 | % A Labeled dataset used for training
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| 8 | % B Additional unlabeled dataset
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| 9 | % N Number of smoothing parameter steps (default 1)
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| 10 | % FID File ID to write progress to (default [], see PRPROGRESS)
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| 11 | %
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| 12 | % OUTPUT
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| 13 | % W Trained classifier, based on A and B
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| 14 | % V Trained classifier based on A only
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| 15 | %
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| 16 | % DESCRIPTION
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| 17 | % Using the EM algorithm the PARZENC classifier is used iteratively
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| 18 | % on the joint dataset [A;B]. In EM each step the labels of A are reset
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| 19 | % to their initial values. Initial labels in B are neglected. They
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| 20 | % are iteratively updated as soft labels obtained by classifying B
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| 21 | % by the actual W. The EM algorithm is run for a fixed smoothing
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| 22 | % parameter of PARZENC. This is repeated for smaller smoothing
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| 23 | % parameters in N steps, using harmonic interpolation between HL and HU,
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| 24 | % in which HL is the smoothing parameter estimate obtained from PARZENML
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| 25 | % applied to A and HU the estimate obtained from PARZENML applied to B.
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| 26 | % For N = 1, the average of HL and HU is used.
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| 27 | %
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| 28 | % SEE ALSO
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| 29 | % DATASETS, MAPPINGS, EMCLUST, EMC, PARZENC, PARZENML, PRPROGRESS
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| 30 |
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| 31 | % Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
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| 32 | % Faculty EWI, Delft University of Technology
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| 33 | % P.O. Box 5031, 2600 GA Delft, The Netherlands
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| 34 |
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| 35 | function [w,v] = emc(a,b,n,fid)
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| 36 | if nargin < 4, fid = []; end
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| 37 | if nargin < 3, n = 1; end
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| 38 | if nargin < 2, b = []; end
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| 39 | if nargin < 1 | isempty(a)
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| 40 | w = mapping(mfilename,'untrained',{b,classf,labtype,fid});
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| 41 | w = setname(w,'EMParzen CLassifier');
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| 42 | return
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| 43 | end
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| 44 |
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| 45 | if size(a,2) ~= size(b,2)
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| 46 | error('Datasets should have same number of features')
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| 47 | end
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| 48 |
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| 49 | c = getsize(a,3);
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| 50 | epsilon = 1e-6;
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| 51 | nlab = getnlab(a);
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| 52 | lablist = getlablist(a);
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| 53 | a = setlabels(a,nlab);
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| 54 | a = setlabtype(a,'soft');
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| 55 | %ws = scalem([+a; +b],'variance');
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| 56 | ws = unitm;
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| 57 | a = a*ws;
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| 58 | b = b*ws;
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| 59 | lab = zeros(size(b,1),c);
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| 60 | hl = parzenml(a);
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| 61 | b = dataset(+b);
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| 62 | hu = parzenml(b);
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| 63 | hl = max(hl,hu) * 1.05;
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| 64 | hu = min(hl,hu) * 0.95;
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| 65 | if n == 1
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| 66 | h = (hl+hu)/2;
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| 67 | else
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| 68 | dh = (log(hl) - log(hu))/(n-1);
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| 69 | h = exp([log(hl):-dh:log(hu)]);
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| 70 | end
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| 71 | c = a;
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| 72 |
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| 73 | first = 1;
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| 74 | for j=1:length(h)
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| 75 | hh = h(j);
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| 76 | prprogress(fid,['\nem_classifier optimization, h = ' num2str(hh) '\n'])
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| 77 | change = 1;
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| 78 | while change > epsilon
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| 79 | w = parzenc(c,hh);
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| 80 | if first, v = w; first = 0; end
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| 81 | d = b*w;
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| 82 | labb = d*classc;
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| 83 | change = mean(mean((+(labb-lab)).^2));
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| 84 | lab = labb;
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| 85 | b = setlabtype(b,'soft',lab);
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| 86 | c = [a; b];
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| 87 | prprogress(fid,' change = %d\n', change)
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| 88 | end
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| 89 | end
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| 90 |
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| 91 | J = getlabels(w);
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| 92 | w = ws*setlabels(w,lablist(J,:));
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| 93 | v = ws*setlabels(v,lablist(J,:));
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| 94 | |
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