An ALM with semismooth Newton-CG and an adaptive sieving strategy solves large-scale support matrix machines with per-iteration cost driven by active samples and solution rank.
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Support matrix machine: exploring sample sparsity, low rank, and adaptive sieving in high-performance computing
An ALM with semismooth Newton-CG and an adaptive sieving strategy solves large-scale support matrix machines with per-iteration cost driven by active samples and solution rank.