Sparse and robust support vector machine with capped squared loss for large-scale pattern classification

Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slo...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition Vol. 153; p. 110544
Main Authors: Wang, Huajun, Zhang, Hongwei, Li, Wenqian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2024
Subjects:
Capped squared loss
Fast algorithm
Low computational complexity
Support vectors
Working set
Working set
Fast algorithm
Support vectors
Capped squared loss
Low computational complexity
ISSN:0031-3203, 1873-5142
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slow. To address this challenge, we construct a novel sparse and robust SVM based on our newly proposed capped squared loss (named as Lcsl-SVM). To solve Lcsl-SVM, we first focus on establishing optimality theory of Lcsl-SVM via our defined proximal stationary point, which is convenient for us to efficiently characterize the Lcsl support vectors of Lcsl-SVM. We subsequently demonstrate that the Lcsl support vectors comprise merely a minor fraction of entire training data. This observation leads us to introduce the concept of the working set. Furthermore, we design a novel subspace fast algorithm with working set (named as Lcsl-ADMM) for solving Lcsl-SVM, which is proven that Lcsl-ADMM has both global convergence and relatively low computational complexity. Finally, numerical experiments show that Lcsl-ADMM has excellent performances in terms of getting the best classification accuracy, using the shortest time and presenting the smallest numbers of support vectors when solving large-scale pattern classification problems. •We establish a novel SVM model called capped squared loss SVM.•We prove the optimality theory for capped squared loss SVM.•We propose a novel subspace fast algorithm with working set to address the capped squared loss SVM.•We demonstrate that our algorithm can efficiently solve the capped squared loss SVM.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2024.110544