| Autor: |
Bertsimas, Dimitris, Pauphilet, Jean, Van Parys, Bart |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Machine Learning, 2021 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s10994-021-06085-5 |
| Popis: |
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 $n0$. |
| Databáze: |
arXiv |
| Externí odkaz: |
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