Anomaly Detection
Given m points in R^n (as a matrix), find the outliers via dimensionality reduction and resampling.
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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 .
General Information
- Version 1.1.0.0 (64.5 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
