TEG, 1 year ago (edited 1 year ago) Some methods to determine the number of components or clusters in PCA or k-means clustering: https://thomasgladwin.substack.com/p/finding-the-true-number-of-components/. These at least work in the limit of ideal simulated data. The basic rationale is to use random split-half data to identify what's "true" versus sampling error. Scores are based on similarities between eigenvectors or cluster centres, rather than, e.g., the shape of the eigenvalue plot. #machineLearning #clustering #kmeans #PCA #scree #python
Some methods to determine the number of components or clusters in PCA or k-means clustering: https://thomasgladwin.substack.com/p/finding-the-true-number-of-components/. These at least work in the limit of ideal simulated data.
The basic rationale is to use random split-half data to identify what's "true" versus sampling error. Scores are based on similarities between eigenvectors or cluster centres, rather than, e.g., the shape of the eigenvalue plot.
#machineLearning #clustering #kmeans #PCA #scree #python
