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


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


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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 ℓ1-Norm Regularized Best-Fit Lines in Optimization Framework
