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Sparse SVM for sufficient data reduction

2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ;ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/tpami.2021.3075339

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Title:
Sparse SVM for sufficient data reduction
Author: Zhou, S
Subjects: 0801 Artificial Intelligence and Image Processing ; 0806 Information Systems ; 0906 Electrical and Electronic Engineering ; Artificial Intelligence & Image Processing ; CLASSIFIER ; Computational efficiency ; Computer Science ; Computer Science, Artificial Intelligence ; Convergence ; Data reduction ; Engineering ; Engineering, Electrical & Electronic ; Fasteners ; Kernel ; math.OC ; Newton method ; one-step convergence property ; OPTIMALITY CONDITIONS ; Optimization ; REGRESSION ; Science & Technology ; sparsity constrained kernel SVM ; SPEED ; SUPPORT VECTOR MACHINE ; Support vector machines ; Technology ; Training
Description: Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in various applications. However, they may incur prohibitive computational costs for large-scale sample datasets. Therefore, data reduction (reducing the number of support vectors) appears to be necessary, which gives rise to the topic of the sparse SVM. Motivated by this problem, the sparsity constrained kernel SVM optimization has been considered in this paper in order to control the number of support vectors. Based on the established optimality conditions associated with the stationary equations, a Newton-type method is developed to handle the sparsity constrained optimization. This method is found to enjoy the one-step convergence property if the starting point is chosen to be close to a local region of a stationary point, thereby leading to a super-high computational speed. Numerical comparisons with several powerful solvers demonstrate that the proposed method performs exceptionally well, particularly for large-scale datasets in terms of a much lower number of support vectors and shorter computational time.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Creation Date: 2021-04
Language: English
Identifier: ISSN: 0162-8828
EISSN: 1939-3539
DOI: 10.1109/tpami.2021.3075339
Source: Spiral

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Sparse SVM for sufficient data reduction

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