Proposes a structured transform learning method via inexact proximal optimization with a closed-form projection, claiming SOTA results on doubly sparse learning at lower cost than dense variants.
Efficient algorithms for solving condition number-constrained matrix minimization problems
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Learning Doubly Sparse Explicitly Conditioned Transforms
Proposes a structured transform learning method via inexact proximal optimization with a closed-form projection, claiming SOTA results on doubly sparse learning at lower cost than dense variants.