SVM for learning with label proportions
SVM for learning with label proportions
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发表时间：
2013-06
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通讯作者：
Felix X. Yu;Dong Liu;Sanjiv Kumar;Tony Jebara;Shih-Fu Chang
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作者：
Felix X. Yu;Dong Liu;Sanjiv Kumar;Tony Jebara;Shih-Fu Chang
我们研究了标签比例的学习问题，其中训练数据是分组提供的，并且只知道每组中每个类的比例。我们提出了一种新的方法，称为proportion-SVM，或∞SVM，它显式地建模潜在的未知实例标签与已知的组标签比例在一个大的利润框架。与现有的作品不同，我们的方法避免了对数据进行限制性假设。∞SVM模型导致非凸整数规划问题。为了有效地解决这个问题，我们提出了两个算法：一个是基于简单交替优化，另一个是基于凸松弛。在标准数据集上的大量实验表明，∞SVM的性能优于最先进的，特别是对于较大的组大小。
We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or ∞SVM, which explicitly models the latent unknown instance labels together with the known group label proportions in a large-margin framework. Unlike the existing works, our approach avoids making restrictive assumptions about the data. The ∞SVM model leads to a non-convex integer programming problem. In order to solve it efficiently, we propose two algorithms: one based on simple alternating optimization and the other based on a convex relaxation. Extensive experiments on standard datasets show that ∞SVM outperforms the state-of-the-art, especially for larger group sizes.