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Poster
Estimating the class prior and posterior from noisy positives and unlabeled data
Shantanu Jain · Martha White · Predrag Radivojac

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #78
in Monday Posters »

We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and parametric and nonparametric algorithms proposed here constitute an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.

Keywords: Kernel Methods · (Other) Statistics · Convex Optimization · Sparsity and Feature Selection · Nonlinear Dimension Reduction and Manifold Learning · Semi-Supervised Learning
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Author Information

Shantanu Jain (Indiana University)
Martha White (University of Alberta)
Predrag Radivojac (Indiana University)

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