
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.


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analysis-of-%E2%84%931-norm-regularized-best-fit-lines-in-optimization-framework


Analysis of ℓ1-Norm Regularized Best-Fit Lines in Optimization Framework


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Proposing an optimization framework for estimating a sparse robust one-dimensional subspace using ℓ1-norm regularization.


ℓ1-norm-regularized-ℓ1-norm-best-fit-lines-optimization-framework-for-sparse-robust-subspace-estimation


ℓ1-Norm Regularized ℓ1-Norm Best-Fit Lines: Optimization Framework for Sparse Robust Subspace Estimation
