Kernel Expansions with Unlabeled Examples

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Martin Szummer, Tommi Jaakkola


Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classi(cid:173) fication performance. We present a new tractable algorithm for exploit(cid:173) ing unlabeled examples in discriminative classification. This is achieved essentially by expanding the input vectors into longer feature vectors via both labeled and unlabeled examples. The resulting classification method can be interpreted as a discriminative kernel density estimate and is read(cid:173) ily trained via the EM algorithm, which in this case is both discriminative and achieves the optimal solution. We provide, in addition, a purely dis(cid:173) criminative formulation of the estimation problem by appealing to the maximum entropy framework. We demonstrate that the proposed ap(cid:173) proach requires very few labeled examples for high classification accu(cid:173) racy.