发表机构
Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SPINE方法，用嵌入1-复形替代点集作为类原型，结合类别条件映射图与分类目标拟合，在17个基准数据集上以匹配预算超越七种原型约简方法，取得最高平均准确率，并在多数数据集上快于广义学习矢量量化。
AI 中文摘要
原型约简用较小的表示替换训练集，已有的方法返回一个有限的点集。我们提出了迭代神经扩张上的骨骼原型（SPINE）。每个类别的模型是一个嵌入的1-复形，而非点集。其初始边集是类别条件映射图，因此数据决定哪些局部簇被连接。后续阶段在分类目标下拟合顶点，观测被分配给其复形最近的类别。因此，线段不仅参与拟合，还进入决策规则。我们在十七个基准数据集上，采用分层10折交叉验证，在匹配的预算下，与七种其他原型约简方法进行了评估。SPINE获得了最高的平均准确率和最佳的平均排名。在Wilcoxon符号秩检验和Holm校正下，它显著优于七个竞争对手中的五个。预算扫描表明，使用整个图线段的决策规则在原型稀缺时贡献最大，而该方法整体在中等预算下表现最佳。构建成本使SPINE属于判别方法，并且在十七个数据集中的十四个上，它比广义学习矢量量化更快。
英文摘要
Prototype reduction replaces a training set with a smaller representation, and the established methods return a finite set of points. We propose Skeletal Prototypes on Iterative Nerve Expansions (SPINE). The model for each class is an embedded 1-complex rather than a point set. Its initial edge set is a class-conditional Mapper graph, so the data decide which localized clusters are joined. Later phases fit the vertices under a classification objective, and an observation is assigned to the class whose complex is nearest. The segments therefore enter the decision rule and not only the fitting. We evaluate SPINE on seventeen benchmark datasets under stratified 10-fold cross validation, against seven other prototype reduction methods at a matched budget. SPINE attains the highest mean accuracy and the best average rank. It is significantly better than five of the seven competitors under Wilcoxon signed-rank tests with Holm correction. A budget sweep shows that the decision rule using the entire graph segments contribute most when prototypes are scarce, while the method as a whole competes best at moderate budgets. Construction cost places SPINE with the discriminative methods, and it is faster than generalized learning vector quantization on fourteen of the seventeen datasets.
