Functional sufficient dimension reduction through information maximization with application to classification

arXiv:2305.10880v3 Announce Type: replace-cross
Abstract: Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information. Compared to the classical FSDR methods, such as functional sliced inverse regression and functional sliced average variance estimation, the proposed methods are appealing because they are capable of estimating multiple effective dimension reduction directions in the case of …

abstract application arxiv case categorical classification cs.lg function functional information loss novel random square stat.ml through type