Publisher = {JMLR Workshop and Conference Proceedings},

Title = {Learning Mixtures of Linear Classifiers},

Url = {http://jmlr.org/proceedings/papers/v32/sunb14.pdf},

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.},

Author = {Yuekai Sun and Stratis Ioannidis and Andrea Montanari},

Editor = {Tony Jebara and Eric P. Xing},

Year = {2014},

Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},

Pages = {721-729}

}