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--- sift:tutorials:outlier_detection_with_pca [2024/08/28 17:54] – [Mahalanobis Distance and SPE Tests] wikisysop
+++ sift:tutorials:outlier_detection_with_pca [2026/04/13 20:26] (current) – updated pca link wikisysop
@@ Line 15: / Line 15: @@
 ==== Local Outlier Factor ====
-[[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.
+[[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
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 For now, we are looking for outliers from within the entire PCA analysis. We could partition it such that points only calculate their LOF against points in their own group or workspace (which we will show later), but for now we want to look at the whole graph as one, and as such we have selected "Combined Groups".
-The number of neighbors is a tuneable parameter within the LOF calculation, and determines how large of a local grouping we will look at. According to the [[https://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf|original paper]] introducing LOF, a k-value below ~10 can cause issues, so we will at least choose this. They also recommend choosing a k of at least the minimum number of points in a cluster. Looking through our data, a k value >= 20 seems reasonable, so we will use k=20.
+The number of neighbors is a tuneable parameter within the LOF calculation, and determines how large of a local grouping we will look at. According to the [[https://dl.acm.org/doi/10.1145/342009.335388|original paper]] introducing LOF, a k-value below ~10 can cause issues, so we will at least choose this. They also recommend choosing a k of at least the minimum number of points in a cluster. Looking through our data, a k value >= 20 seems reasonable, so we will use k=20.
 We choose to use 2 PCs, as the workspace scores are presented in 2 dimensions.