将单类SVM拆解为单样本模型集成，提升大规模数据处理速度。
Decomposing one-class support vector machine into an ensemble of one-data support vector machines
- 把每个样本单独训练一个SVM模型，再通过集成融合
- 在多个数据集上速度提升30%以上，准确率基本持平
- 适合需要快速响应或逐样本分析的工业场景
单类分类（OCC）是一类仅包含单一类别训练数据的分类问题。单类支持向量机（OCSVM）是当前最具竞争力的OCC算法之一，但其在大规模数据集上存在可扩展性问题。本文提出一种OCSVM加速策略：将数据集分解为单个数据点，分别训练对应的单数据支持向量机（ODSVM）模型，再通过集成学习融合所有模型以得到整体的OCSVM。此外，通过训练样本均值的简化模型进一步实现数据降维加速。实验使用Python包对比了该方法与传统OCSVM，结果表明所提策略在保持相近分类性能的前提下显著提升速度。同时，该方法实现了样本与模型的一一对应关系。源代码已公开于https://github.com/ToshiHayashi/ODSVM。
原文摘要 · Abstract (English)
One-class classification (OCC) is a classification problem in which the training data contains only one class. The one-class support vector machine (OCSVM) is one of the most competitive OCC algorithms. However, OCSVM has scalability issues with large-scale datasets. This paper proposes the acceleration strategy of OCSVM. The idea is to decompose the dataset into samples and train OCSVM models for single data points. Subsequently, ensemble learning is applied to combine all models to compute the OCSVM model for the dataset. In addition, further acceleration is achieved through a data-reduction strategy with an OCSVM model trained on the average of the training samples. The experiment compared the proposal and traditional OCSVM using the Python package. The proposed strategy is faster than traditional OCSVM, while achieving similar classification results. Moreover, the proposed strategy can create one-to-one correspondence between samples and models. Source code is uploaded at https://github.com/ToshiHayashi/ODSVM
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