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Found 2 papers, 1 papers with code
Safe Feature Elimination for Non-Negativity Constrained Convex Optimization
Under reasonable conditions, our feature elimination strategy will eventually eliminate all zero features from the problem.
Efficient Solvers for Sparse Subspace Clustering
The $\ell_0$ model is non-convex but only needs memory linear in $n$, and is solved via orthogonal matching pursuit and cannot handle the case of affine subspaces.
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