The author proposes an optimization framework for estimating a sparse robust one-dimensional subspace using ℓ1-norm regularization. The approach aims to minimize representation error and penalty, achieving global optimality for the sparse robust subspace.
Proposing an optimization framework for estimating a sparse robust one-dimensional subspace using ℓ1-norm regularization.