Federated Optimization of ℓ0-norm Regularized Sparse Learning
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DOI: 10.3390/a15090319
Abstract: Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering… read more here.
Keywords: regularized sparse; federated optimization; hard thresholding; sparse learning ... See more keywords