Sparse Classification: a scalable discrete optimization perspective

Abstract

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. On synthetic data our algorithm achieves perfect support recovery in the large sample regime. Namely, there exists a $n_0$ such that the algorithm takes a long time to find the optimal solution and does not recover the correct support for $n 0$.