Article

Abstract

• In this work, we present an efficient method for estimating active subspaces using only random observations of gradient vectors. Our method is based on the bi-linear representation of low-rank gradient matrices with a novel initialization step for alternating minimization.

Creator

• Pourkamali-Anaraki, Farhad
• Becker, Stephen

Date Issued

• 2017-07-01

Academic Affiliation

• Applied Mathematics

Journal Title

• SIAM Workshop on Dimension Reduction 2017

Última modificação

• 2019-12-05

Resource Type

• Article

Declaração de direitos

• In Copyright

Language

• English [eng]

Relações

Itens

Miniatura	Título	Data de carga	Acesso	Ações
	estimatingActiveSubspacesWithRandomizedGradientSampling.pdf	2019-12-05	Público	Baixar