[10] | 1 | %PE_DISTM Square Pseudo-Euclidean (PE) Distance Between Two Datasets |
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| 2 | % |
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| 3 | % D = PE_DISTM(A) |
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| 4 | % OR |
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| 5 | % D = PE_DISTM(A,B) |
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| 6 | % |
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| 7 | % INPUT |
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| 8 | % A PE dataset of size NxK |
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| 9 | % B PE dataset of size MxK (default A = B) |
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| 10 | % |
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| 11 | % OUTPUT |
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| 12 | % D NxM dissimilarity matrix or dataset |
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| 13 | % |
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| 14 | % DESCRIPTION |
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| 15 | % Computation of the square pseudo-Euclidean distance matrix D between two sets |
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| 16 | % of vectors, A and B. The pseudo-Euclidean distance with the signature SIG |
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| 17 | % (e.g. SIG = [10 5]) between vectors X and Y is computed as an indefinite |
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| 18 | % 'Euclidean' distance: |
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| 19 | % D(X,Y) = (X-Y)'*J*(X-Y), |
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| 20 | % where J is a diagonal matrix with 1's, followed by -1's. |
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| 21 | % J = diag ([ONES(SIG(1),1); -ONES(SIG(2),1)]); |
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| 22 | % |
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| 23 | % In a PE dataset the signature is stored in the user field, see |
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| 24 | % SETSIG. This signature is derived from A. It is not stored in D as D |
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| 25 | % does not contain vectors in a PE space. |
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| 26 | % |
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| 27 | % D is a dataset with the labels defined by the labels of A and feature labels |
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| 28 | % defined by the labels of B. |
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| 29 | % |
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| 30 | % REMARKS |
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| 31 | % Note that square pseudo-Euclidean distances can be negative. |
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| 32 | % |
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| 33 | % SEE ALSO |
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| 34 | % DATASET, SETSIG, DISTM |
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| 35 | |
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| 36 | % Copyright: Elzbieta Pekalska, ela.pekalska@googlemail.com |
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| 37 | % Faculty EWI, Delft University of Technology and |
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| 38 | % School of Computer Science, University of Manchester |
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| 39 | |
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| 40 | |
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| 41 | |
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| 42 | function D = pe_distm(A,B) |
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| 43 | |
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| 44 | isdataset(A); |
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| 45 | bisa = 0; |
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| 46 | if nargin < 2, B = A; bisa = 1; end |
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| 47 | sig = getsig(A); |
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| 48 | |
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| 49 | if ~isdataset(B), B = dataset(B,1); end |
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| 50 | |
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| 51 | a = +A; |
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| 52 | b = +B; |
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| 53 | [ra,ca] = size(a); |
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| 54 | [rb,cb] = size(b); |
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| 55 | |
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| 56 | if ca ~= cb, |
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| 57 | error ('The datasets should have the same number of features.'); |
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| 58 | end |
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| 59 | |
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| 60 | if any(sig) < 0 | sum(sig) ~= ca, |
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| 61 | error('Signature vector SIG is invalid: its sum should equal feature size'); |
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| 62 | end |
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| 63 | |
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| 64 | J = [ones(1,sig(1)) -ones(1,sig(2))]; |
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| 65 | D = - 2 .* a * diag(J) * b'; |
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| 66 | D = D + ones(ra,1) * (J*(b'.*b')); |
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| 67 | D = D + (J * (a'.*a'))' * ones(1,rb); |
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| 68 | |
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| 69 | if bisa |
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| 70 | D = 0.5*(D+D'); % Make sure that distances are symmetric for D(A,A) |
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| 71 | end |
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| 72 | |
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| 73 | % Set object labels and feature labels |
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| 74 | D = setdata(A,D,getlab(B)); |
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| 75 | D.name = 'Square Pseudo-Euclidean distance matrix'; |
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| 76 | if ~isempty(A.name) |
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| 77 | D.name = [D.name ' for ' A.name]; |
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| 78 | end |
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| 79 | |
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| 80 | return |
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