Abstract
Estimating the number of principal components to retain for dimension reduction is a critical step in many applications of principal component analysis. Common methods may not be optimal, however. The current paper presents an alternative procedure that aims to recover the true number of principal components, in the sense of the number of independent vectors involved in the generation of the data.•Data are split into random halves repeatedly.•For each split, the eigenvectors in one half are compared to those in the other.•The split between high and low similarities is used to estimate the number of principal components.The method is a proof of principle that similarity over split-halves of the data may provide a useful approach to estimating the number of components in dimension reduction, or of similar dimensions in other models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
