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Sparse classification: a scalable discrete optimization perspective
Name
10994_2021_6085_ReferencePDF.pdf
Size
964.95 KB
Format
Adobe PDF
Checksum (MD5)
890e8c74cc52f3528941923d1d55edd0
Author(s)
Bertsimas, Dimitris
Pauphilet, Jean
Van Parys, Bart
Date Issued
November 2, 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
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
.
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
