Article

 

ESTIMATING ACTIVE SUBSPACES WITH RANDOMIZED GRADIENT SAMPLING 公开 Deposited

https://scholar.colorado.edu/concern/articles/td96k311g
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.
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Date Issued
  • 2017-07-01
Academic Affiliation
Journal Title
最新修改
  • 2019-12-05
Resource Type
权利声明
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