Sift - Mahalanobis Distance Dialog

Mahalanobis Distance is a common measure used to determine outliers in a data sample. The Mahalanobis distance can be conceptualized as the distance of a point from the centroid of the data set onto an ellipse. The Mahalanobis distance method can be used on PCA results. This is done by measuring the distance of each point to the centroid in the transformed PCA space.

The Mahalanobis Distance is found on the toolbar and under 'Outlier Detecting Using PCA' in the Analysis menu.

Dialog

The Mahalanobis Distance using PCA allows the user to automatically search the data and identify traces that are outside the norm. The search for outliers can be done by Combined Groups, Group, and Workspaces. Users can decide if they want to auto-exclude any outliers that are detected in the groups or workspaces, and specify the retained cumulative variability of the model by the PCs.