Large Margin Multi-Task Metric Learning
Large Margin Multi-Task Metric Learning
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发表时间：
2010-12
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通讯作者：
Shibin Parameswaran;Kilian Q. Weinberger
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作者：
Shibin Parameswaran;Kilian Q. Weinberger
多任务学习(MTL)通过共享参数或表示来提高对多个不同但相关的学习问题的预测性能。最著名的多任务学习算法之一是Evgeniou等人对支持向量机的扩展。[15]。尽管非常优秀，但多任务支持向量机固有地受到以下事实的限制，即支持向量机需要用其自己的权重向量明确地对每个类进行寻址，这在多任务设置中要求不同的学习任务共享相同的类集合。通过将最近发表的大间隔最近邻(1mnn)算法扩展到MTL范式，提出了一种用于多任务学习的替代公式。它的决策函数不依赖于分离的超平面，而是基于最近邻规则，该规则固有地扩展到多个类别，成为一种自然适合于多任务学习的规则。我们在现实世界的保险数据和语音分类问题上对得到的多任务1mnn进行了评估，结果表明，在多种度量和最先进的MTL分类器下，它的性能一致优于单任务knn。
Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to support vector machines (svm) by Evgeniou et al. [15]. Although very elegant, multi-task svm is inherently restricted by the fact that support vector machines require each class to be addressed explicitly with its own weight vector which, in a multi-task setting, requires the different learning tasks to share the same set of classes. This paper proposes an alternative formulation for multi-task learning by extending the recently published large margin nearest neighbor (1mnn) algorithm to the MTL paradigm. Instead of relying on separating hyperplanes, its decision function is based on the nearest neighbor rule which inherently extends to many classes and becomes a natural fit for multi-task learning. We evaluate the resulting multi-task 1mnn on real-world insurance data and speech classification problems and show that it consistently outperforms single-task kNN under several metrics and state-of-the-art MTL classifiers.