matlab 计算大型距离方阵，distance matrix

转：http://blog.csdn.net/yihaizhiyan/article/details/19410793

利用vlfeat工具包可行。

网址见：http://www.vlfeat.org/matlab/vl_alldist2.html

返回： pairwise distance matrix D of the columns of S1 and S2, yielding

D = VL_ALLDIST2(X,Y) returns the pairwise distance matrix D of the columns of S1 and S2, yielding

D(i,j) = sum (X(:,i) - Y(:,j)).^2

VL_ALLDIST2(X) returns the pairwise distance matrix fo the columns of S, yielding

D(i,j) = sum (X(:,i) - X(:,j)).^2

VL_ALLDIST2(…,’METRIC’) changes the computed distance. Supported values for METRIC are

METRIC D(i,j)

LINF max |X - Y|

L2 sum (X - Y).^2

L1 sum |X - Y|

L0 sum (X ~= Y)

CHI2 sum (X - Y).^2 ./ (X + Y)

HELL sum (X^.5 - Y^.5) .^ 2

(Notice that the standard definition of chi2 is half of what is computed here).

VL_ALLDIST2(…,’KERNEL’) computes the following ‘kernels’ K:

KERNEL K(i,j)

KL2 sum X .* Y

KL1 sum min (X, Y)

KCHI2 2 * sum (X .* Y) ./ (X + Y)

KHELL (X .* Y) .^ 0.5

The constant are chosen so that D(i,j) = K(i,i) + K(j,j) - 2 K(i,j) where D is the metric corresponding to the kenrel (if the arguments are non-negative vectors). Each kernel can be interpreted as the inner product inducing the corresponding metric in an embedding of the real space into an approrpiate reproducing Kenrel Hilbert space.

VL_ALLDIST2() supports several storage classes. X and Y must have the same storage class. The sotrage class of D is promoted to reduce the chance of overvlow, but this is not checked.

转自：http://www.vlfeat.org/matlab/vl_alldist2.html

http://www.vlfeat.org/matlab/matlab.html