Hi RUsers,
I am struggling to come up with an efficient vectorized way to convert
20Kx20K covariance matrix to a Euclidian distance matrix as a surrogate for
dissimilarity matrix. Hopefully I can apply multidimensional scaling for
mapping these 20K points (commercial products).
I understand that Distance(ij) = sigma(i) + sigma(j) - 2cov(ij). Without
replying on a slow loop, I appreciate if anyone can help me out with a
better idea - guess lapply?
Thank you very much.
Taka
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