Anomaly Detection

Version 1.1.0.0 (64.5 KB) by michael kim
Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling.
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Updated 27 Dec 2012

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Given a matrix with m rows and n cols (m points in R^n), use resampling and the Kolmogorov Smirnov test to score [0,1] all points (as potential outliers) in linear time.

This is an original algorithm that can be used for anomaly detection and general signal processing.

Cite As

michael kim (2026). Anomaly Detection (https://www.mathworks.com/matlabcentral/fileexchange/39593-anomaly-detection), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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kse_test_matlab/

Version Published Release Notes
1.1.0.0

This is a port from Octave code. Fixed some issues with the Octave to Matlab conversion.
1.0.0.0