Abstract
SAVE and PHD are effective methods in dimension reduction problems. Both methods are based on two assumptions: linearity condition and constant covariance condition. But in the situation where constant covariance condition fails, even if linearity condition holds, SAVE and PHD often pick the directions which are out side of the central subspace (CS) or central mean subspace (CMS). In this article, we generalize the SAVE and PHD under weaker conditions. This generalization make it possible to get the correct estimates of central subspace (CS) and central mean subspace (CMS).
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.
