Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods
Thanks to PCA’s sensitivity, it can be used to detect outliers in multivariate datasets. —  Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction while preserving relevant information. Due to its sensitivity, it can also be used to detect outliers in multivariate datasets. Outlier detection can provide early warning signals for abnormal conditions, allowing experts to identify and address issues before they…
Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods Thanks to PCA’s sensitivity, it can be used to detect outliers in multivariate datasets. —  Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction while preserving relevant information. Due to its sensitivity, it can also be used to detect outliers in multivariate datasets. Outlier detection can provide early warning signals for abnormal conditions, allowing experts to identify and address issues before they… data-science
Outlier Detection Using Principal Component Analysis Hotelling’s T2 SPEDmodX Methods Thanks PCA’s sensitivity used detect outlier multivariate datasets — Principal Component Analysis PCA widely used technique dimensionality reduction preserving relevant information Due sensitivity also used detect outlier multivariate datasets Outlier detection provide early warning signal abnormal condition allowing expert identify address issue they… datascience