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Z-score-normalized euclidean distances

version (2.87 KB) by Weirong Chen
Compute normalized euclidean distance between two arrays [m (points) x n (features)]


Updated 03 Oct 2016

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% Z-score-normalized euclidean distances.
% Compute euclidean distance between two arrays [m (points) x n (features)]
% The two input arrays must share the same features but each feature may be
% in different scale (e.g., Time (ms) vs. Freq (kHz) ). In order to compute
% the closeness between two arrays without weighting on the feature with
% larger scale, the two input arrays undergo z-score
% normalization, before computing the distances.
% Input:
% pt1, pt2 : two input arrays: m (points) x n (features)
% pt1 and pt1 must be either in the same dimensions (i.e., dim(pt1) = dim(pt2) ), or
% pt2 = 1 x n (features) and pt1 = m (points) x n (features),
% then the output 'd' will be the distances from pt2 to each elements in pt1.
% Output:
% d : m (points) x 1 vector: zscore normalized distances/closeness between the two input arrays.
% Wr.Ch Jun-15-2016

Cite As

Weirong Chen (2020). Z-score-normalized euclidean distances (, MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R14
Compatible with any release
Platform Compatibility
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