Differences

This shows you the differences between two versions of the page.

--- sift:tutorials:outlier_detection_with_pca [2026/03/10 18:08] – wikisysop
+++ sift:tutorials:outlier_detection_with_pca [2026/04/13 20:26] (current) – updated pca link wikisysop
@@ Line 17: / Line 17: @@
 [[Sift:Principal_Component_Analysis:Outlier_Detection_for_PCA#Local_Outlier_Factor|Local Outlier Factor (LOF)]] is a outlier detection method that uses the local density around data points to determine if a point is an outlier. In this sense it can find outliers that global detection methods would not, as it identifies outliers in local areas.
-In Sift, LOF is built upon the [[Sift:Principal_Component_Analysis:Using_Principal_Component_Analysis_in_Biomechanics|PCA module]], to find outliers in the PC workspace scores. As such, we will need to create a PCA analysis. To show the benefits of Local Outlier Factor, we will be using the group "HipAngle_Z", as it has a good shape to demonstrate the effectiveness of LOF (multiple clusters of varying density). Specifically, create a PCA with:
+In Sift, LOF is built upon the [[Sift:Principal_Component_Analysis|PCA module]], to find outliers in the PC workspace scores. As such, we will need to create a PCA analysis. To show the benefits of Local Outlier Factor, we will be using the group "HipAngle_Z", as it has a good shape to demonstrate the effectiveness of LOF (multiple clusters of varying density). Specifically, create a PCA with:
   * HipAngle_Z selected (and all workspaces)
   * 4 PCs calculated