IEEE Systems, Man and Cybernetics Magazine - April 2020 - 12

Pseudocode for Various Algorithms Belonging to the Visual Assessment
of Tendency Family
The MATLAB implementations for selected algorithms are available on Github [1].

Algorithm S1. Visual Assessment of
Tendency (VAT) [11].
Input : D - n # n dissimilarity matrix satisfying
				 - D ij $ 0
				 - D ij = D ji 6i, j
				 - D ii = 0 6 i
Output: D ) - n # n VAT reordered dissimilarity matrix
		 I (D )) - VAT image of D )
		 P -  VAT reordering indices of D
		 d -  ordering of MST cut magnitudes
	1	Set K = " 1, 2,f, n ,, I = J = 4
	2	Select (i, j ) ! argmax D kq
k ! K, q ! K

	3	P1 = i ; I = " i , and J = K - " i ,
	 4	for t ! 2 to n do
	 5		 Select (i, j ) ! argmin D kq

	1	Compute a local scale vi for each object o i
	 2	v i = d (o i , o K ) = d iK , where o K is the kth nearest
  neighbor of o i
	3	Construct weight matrix W ! R n # n
	4	Construct diagonal matrix M with m ii = | nj = 1 w ij
	 5	Construct normalized Laplacian matrix: L l = M -1/2WM -1/2
	6	Choose v 1,v 2,f,v k , , the k largest eigenvectors of L
	7	V = [v 1,f,v k ] ! R n # k
	8	Normalize the rows of V to generate V l :v ijl = v ij /


IEEE Systems, Man and Cybernetics Magazine - April 2020
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