Locally linear SVMs based on boundary anchor points encoding. (September 2019) - Xu, Baile; Shen, Shaofeng; Shen, Furao; Zhao, Jian
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.