Sparse Classification and Phase Transitions: A Discrete Optimization Perspective

In this paper, we formulate the sparse classification problem of $n$ samples with $p$ features as a binary convex optimization problem and propose a cutting-plane algorithm to solve it exactly. For sparse logistic regression and sparse SVM our algorithm finds optimal solutions for $n$ and $p$ in the $10,000$s within minutes... (read more)

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