CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-08-12
Cited: 0
Clicked: 7241
Lei Guan, Tao Sun, Lin-bo Qiao, Zhi-hui Yang, Dong-sheng Li, Ke-shi Ge, Xi-cheng Lu. An efficient parallel and distributed solution to nonconvex penalized linear SVMs[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(4): 587-603.
@article{title="An efficient parallel and distributed solution to nonconvex penalized linear SVMs",
author="Lei Guan, Tao Sun, Lin-bo Qiao, Zhi-hui Yang, Dong-sheng Li, Ke-shi Ge, Xi-cheng Lu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="4",
pages="587-603",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800566"
}
%0 Journal Article
%T An efficient parallel and distributed solution to nonconvex penalized linear SVMs
%A Lei Guan
%A Tao Sun
%A Lin-bo Qiao
%A Zhi-hui Yang
%A Dong-sheng Li
%A Ke-shi Ge
%A Xi-cheng Lu
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 4
%P 587-603
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800566
TY - JOUR
T1 - An efficient parallel and distributed solution to nonconvex penalized linear SVMs
A1 - Lei Guan
A1 - Tao Sun
A1 - Lin-bo Qiao
A1 - Zhi-hui Yang
A1 - Dong-sheng Li
A1 - Ke-shi Ge
A1 - Xi-cheng Lu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 4
SP - 587
EP - 603
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800566
Abstract: Support vector machines (SVMs) have been recognized as a powerful tool to perform linear classification. When combined with the sparsity-inducing nonconvex penalty, SVMs can perform classification and variable selection simultaneously. However, the nonconvex penalized SVMs in general cannot be solved globally and efficiently due to their nondifferentiability, nonconvexity, and nonsmoothness. Existing solutions to the nonconvex penalized SVMs typically solve this problem in a serial fashion, which are unable to fully use the parallel computing power of modern multi-core machines. On the other hand, the fact that many real-world data are stored in a distributed manner urgently calls for a parallel and distributed solution to the nonconvex penalized SVMs. To circumvent this challenge, we propose an efficient alternating direction method of multipliers (ADMM) based algorithm that solves the nonconvex penalized SVMs in a parallel and distributed way. We design many useful techniques to decrease the computation and synchronization cost of the proposed parallel algorithm. The time complexity analysis demonstrates the low time complexity of the proposed parallel algorithm. Moreover, the convergence of the parallel algorithm is guaranteed. Experimental evaluations on four LIBSVM benchmark datasets demonstrate the efficiency of the proposed parallel algorithm.
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