Locally linear SVMs based on boundary anchor points encoding. (September 2019)

• Record Type: Journal Article
• Authors: Xu, Baile; Shen, Shaofeng; Shen, Furao; Zhao, Jian
• Is Part Of: Neural networks. Volume 117(2019)
• Page Start: 274
• Page End: 284
• Publication Date: 2019-09
• Subjects: Locally linear classifier -- Kernel SVM -- Boundary points -- Large-scale data; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.05.023
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we propose a locally linear classifier based on boundary anchor points encoding (LLBAP) to achieve the efficiency of linear SVM and the power of kernel SVM. LLBAP partitions linearly non-separable data into approximately linearly separable parts based on boundary point scanning and local coding. Each part of data is solved by a linear SVM. Experiments on large-scale benchmark datasets demonstrate that the proposed method is more efficient than kernel SVM in both training and testing phases; its efficiency and classification accuracy also outperform other locally linear classifiers on those benchmark datasets.