A local approach to parameter space reduction for regression and classification tasks

arXiv:2107.10867v3 Announce Type: replace
Abstract: Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and surrogate models' design, especially when in presence of high-dimensional parametrized systems. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter …

abstract analysis arxiv classification cs.na design math.na numerical optimization regression sensitivity space speed stat.ml systems tasks tool type work