提出高效算法训练大规模多分类的极小极大风险分类器。
Efficient Large-Scale Learning of Minimax Risk Classifiers
- 结合约束与列生成技术，解决极小极大风险分类的优化难题。
- 在多类任务中实现10倍至100倍的训练加速。
- 适合需要鲁棒性保障的大规模多分类场景。
大规模监督学习常导致复杂的优化问题，尤其在多类别分类任务中。虽然随机次梯度方法能高效处理平均损失最小化的分类方法，但近期提出的极小极大风险分类器（MRCs）旨在最小化最大期望损失，难以适用此类方法。本文提出一种基于约束与列生成相结合的学习算法，实现了多类别分类任务下大尺度数据上MRCs的高效学习。在多个基准数据集上的实验表明，该算法对一般大规模数据可提升约10倍速度，当类别数量显著时更可达约100倍加速。
原文摘要 · Abstract (English)
Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods can enable efficient learning with a large number of samples for classification techniques that minimize the average loss over the training samples. However, recent techniques, such as minimax risk classifiers (MRCs), minimize the maximum expected loss and are not amenable to stochastic subgradient methods. In this paper, we present a learning algorithm based on the combination of constraint and column generation that enables efficient learning of MRCs with large-scale data for classification tasks with multiple classes. Experiments on multiple benchmark datasets show that the proposed algorithm provides upto a 10x speedup for general large-scale data and around a 100x speedup with a sizeable number of classes.
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