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arXiv · 1311.2547

Learning Mixtures of Linear Classifiers

Yuekai Sun·Stratis Ioannidis·Andrea Montanari
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Abstract

We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers. Existing approaches are based on the EM algorithm, or similar techniques, without provable guarantees. We develop a simple method based on spectral techniques and a `mirroring' trick, that discovers the subspace spanned by the classifiers' parameter vectors. Under a probabilistic assumption on the feature vector distribution, we prove that this approach has nearly optimal statistical efficiency.

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Yuekai Sun, Stratis Ioannidis, Andrea Montanari. 2014-07-30. Learning Mixtures of Linear Classifiers. https://arxiv.org/abs/1311.2547

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