Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Random forest models classify VLASS DRAGNs by artifact count with 97 percent weighted F1 score, enabling extraction of a high-completeness artifact-free catalog.
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Faster than Fast-LTS: Robust Regression and Outlier Detection with DC Programming
Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.