$\propto$SVM for learning with label proportions

By Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang. 2013

59 citations recorded by the source corpus.

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 $\propto$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 $\propto$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 $\propto$SVM outperforms the state-of-the-art, especially for larger group sizes.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zechao Li
2. Hanqing Lu
3. Zhiqiang Tang
4. Yifan Zhang
5. Giorgio Patrini
6. Richard Nock

Citations in the corpus

• Weakly supervised learning via statistical sufficiency (2016, methodology): “Since we test on similar domains (6 are actually the same), hyper-parameters ranges for InvCal and ∝SVM are taken from [Yu et al., 2013].”
• Face Clustering in Videos with Proportion Prior (2015, methodology): “Yu et al. [Yu et al., 2013] proposed a method called proportion-SVM, which explicitly models the latent unknown instance labels together with the known group label proportions in a large-margin framework.”