Sparse classification: a scalable discrete optimization perspective
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10994_2021_6085_ReferencePDF.pdf
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Author(s)
Bertsimas, Dimitris J
Pauphilet, Jean
Van Parys, Bart Paul Gerard
Date Issued
November 2021
Publisher
Springer US
Citation
Bertsimas, Dimitris, Pauphilet, Jean and Van Parys, Bart. 2021. "Sparse classification: a scalable discrete optimization perspective."
Version
Author's final manuscript
Abstract
We formulate the sparse classification problem of n samples with p features as a binary convex optimization problem and propose a outer-approximation 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 an
$$n_0$$
n
0
such that the algorithm takes a long time to find an optimal solution and does not recover the correct support for
$$n0$$
C
>
0
.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-021-06085-5
