Title: Information-Theoretic Representation Learning for Positive-Unlabeled Classification,
Abstract: Recent advances in weakly supervised classification allow us to train a
classifier only from positive and unlabeled (PU) data. However, existing PU
classification methods typically require an accurate estimate of the
class-prior probability, which is a critical bottleneck particularly for
high-dimensional data. This problem has been commonly addressed by applying
principal component analysis in advance, but such unsupervised dimension
reduction can collapse underlying class structure. In this paper, we propose a
novel representation learning method from PU data based on the
information-maximization principle. Our method does not require class-prior
estimation and thus can be used as a preprocessing method for PU
classification. Through experiments, we demonstrate that our method combined
with deep neural networks highly improves the accuracy of PU class-prior
estimation, leading to state-of-the-art PU classification performance.