A penalized continuous mRMR objective with SCAD/MCP penalties and a knockoff filter is proposed for FDR-controlled feature screening, with sparsistency theory and experiments.
Ultrahigh dimensional feature screening via RKHS embeddings
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Sparse minimum Redundancy Maximum Relevance for feature selection
A penalized continuous mRMR objective with SCAD/MCP penalties and a knockoff filter is proposed for FDR-controlled feature screening, with sparsistency theory and experiments.