Outlier Detection Using Principal Component Analysis with Hotelling's T2 and SPE/DmodX Methods

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Outlier Detection Using Principal Component Analysis with Hotelling's T2 and SPE/DmodX Methods

Take advantage of the sensitivity of PCA: A step-by-step guide for outlier detection in multivariate datasets.

Principal Component Analysis (PCA) is a widely used statistical method for dimensionality reduction that preserves relevant information. PCA has a very high sensitivity that can also be used for detecting outliers in multivariate datasets. Or in other words, if your work requires early warning signals for abnormal conditions, where transparent and explainable results are required, PCA is an excellent choice. However, detecting outliers in multivariate datasets can be challenging due to the high dimensionality and the lack of labels. PCA offers several advantages for outlier detection.

In this blog, I will describe the concepts and benefits of outlier detection using PCA. I will demonstrate, with hands-on examples, how to create unsupervised outlier detection models for continuous and categorical data sets.