continuous search of the closest rows in a matrix

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pavlos
pavlos 2013-1-15
Hello,
Consider a 100x10 matrix and a random row of it, let be "row1".
I want to calculate the Euclidean distance between "row1" and the rest of the rows and I want to find the closest row to "row1", let be "row2".
Then I want to find the closest row to "row2", let be "row3" (the "row1" has been excluded from the matrix) and so on.
I use the "pdist2" for the Euclidean distance.
How you please help me?
Thank you.
Best,
Pavlos
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José-Luis
José-Luis 2013-1-15
Please post what you have done so we can help you accordingly.
pavlos
pavlos 2013-1-15
Here is the code:
%extract a random row from the matrix M 100x10
T = randperm(100);
row1 = M(T(1:1),:);
%remove the row from the matrix
M(T(1),:)=[];
%calculate the distances between M and row1
d1=pdist2(M,row1);
%find the closest row to row1 and the corresponding distance between them
[min_dis ind_min_dis]=min(d1);
row2=T(ind_min_dis,:);
%remove row2 from the matrix
M(ind_min_dis,:)=[];
%continue the same process with a loop
for k=1:length(M)
d(k)=pdist2(M,row(k));
[min_dis(k) ind_min_dis(k)]=min(d(k));
row(k)=M(ind_min_dis(k),:);
M(ind_min_dis(k),:)=[];
end

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Matt J
Matt J 2013-1-15
编辑：Matt J 2013-1-15
Here's what I use. There are others like it on the FEX, some of them fancier.
function Graph=interdists(A,B)
%Finds the graph of distances between point coordinates
%
% (1) Graph=interdists(A,B)
%
% in:
%
% A: matrix whose columns are coordinates of points, for example
% [[x1;y1;z1], [x2;y2;z2] ,..., [xM;yM;zM]]
% but the columns may be points in a space of any dimension, not just 3D.
%
% B: A second matrix whose columns are coordinates of points in the same
% Euclidean space. Default B=A.
%
%
% out:
%
% Graph: The MxN matrix of separation distances in l2 norm between the coordinates.
% Namely, Graph(i,j) will be the distance between A(:,i) and B(:,j).
%
%
% (2) interdists(A,'noself') is the same as interdists(A), except the output
% diagonals will be NaN instead of zero. Hence, for example, operations
% like min(interdists(A,'noself')) will ignore self-distances.
%
% See also getgraph
noself=false;
if nargin<2
B=A;
elseif ischar(B)&&strcmpi(B,'noself')
noself=true;
B=A;
end
N=size(A,1);
B=reshape(B,N,1,[]);
Graph=l2norm(bsxfun(@minus, A, B),1);
Graph=squeeze(Graph);
if noself
n=length(Graph);
Graph(linspace(1,n^2,n))=nan;
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Teja Muppirala
Teja Muppirala 2013-1-16
If you have the Statistics Toolbox installed, instead of using PDIST2, use PDIST (along with SQUAREFORM) instead. It is designed to find all interpoint distances for a single matrix very efficiently. Then your entire problem could be reduced to this:
M = randn(100,10);
L = size(R,1);
D = squareform(pdist(M));
D(1:L+1:L^2) = nan;
startrow = 1; % Starting Row
row = [startrow; zeros(L-1,1)];
for n = 2:L
oldrow = startrow;
[val,startrow] = min(D(:,startrow));
D(oldrow,:) = nan;
row(n) = startrow;
end
row

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