This article continues a series related to applications of PCA (principle component analysis) for outlier detection, following Using PCA for Outlier Detection. That article described PCA itself, and…

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PCA (Principal Component Analysis) is used to transform data into a new set of features, improving the performance of outlier detection. This post explains how PCA can reduce the complexity of data and make outliers more detectable. It discusses both univariate and multivariate outlier detection techniques and provides examples using synthetic datasets. The key advantages of PCA include improved detection accuracy, faster execution times, and reduced memory usage. Practical implementation with PyOD detectors is also demonstrated.

A Simple Example Using PCA for Outlier Detection

Univariate and Multivariate outer detectors

Improving the outlier detection system over time