State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, China.
School of Electronic Science and Engineering, Nanjing University, China.
Neural Netw. 2019 Sep;117:274-284. doi: 10.1016/j.neunet.2019.05.023. Epub 2019 May 30.
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.
在本文中，我们提出了一种基于边界锚点编码的局部线性分类器（LLBAP），以实现线性 SVM 的效率和核 SVM 的威力。LLBAP 通过边界点扫描和局部编码将线性不可分数据划分为近似线性可分的部分。数据的每一部分都由线性 SVM 解决。在大规模基准数据集上的实验表明，与核 SVM 相比，该方法在训练和测试阶段都更高效；在这些基准数据集上，它的效率和分类精度也优于其他局部线性分类器。
