提出一种新算法，让单类支持向量机在有额外训练信息时表现更优。
Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information
- 基于SMO思想设计新算法，专为带特权信息的单类SVM优化
- 训练速度显著快于非序列方法，且能保证有限步内收敛
- 适合需要高效训练、有训练期额外信息的异常检测场景
数据建模中一个重要技术是利用训练阶段可用但测试时不可用的特征——即使用特权信息（LUPI）范式。已有顺序最小优化（SMO）方法适用于监督SVM、无监督单类SVM以及带特权信息的SVM（SVM+）。然而，缺少针对带特权信息的单类SVM（OC-SVM+）的SMO算法。本文提出一种OC-SVM+的SMO算法，其训练效率显著优于非序列算法，并证明了该算法具有有限时间收敛性。实验揭示了特权信息对原始特征空间中描述性域的影响。对比基准测试表明，本算法优于内点法。
原文摘要 · Abstract (English)
One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data -- the Learning Using Privileged Information paradigm (LUPI). Sequential Minimal Optimization (SMO) methods have been developed for supervised Support Vector Machines (SVM), unsupervised one-class SVM, and SVM with privileged information (SVM+). The missing brick in this research has long been a one-class SVM with privileged information (OC-SVM+). In this paper, we propose an SMO algorithm for OC-SVM+ that significantly outperforms non-sequential algorithms for training the OC-SVM+ model. Its finite-time convergence is established. The experiments show how privileged information affects a descriptive domain in the space of original features. Comparative benchmark tests demonstrate that our algorithm is superior over interior point algorithms.
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