Recent advances in weakly supervised classification allow us to train a classifier from only positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, 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 the underlying class structure. In this letter, 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.

中文翻译：

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用于正未标记分类的信息论表示学习

弱监督分类的最新进展使我们能够仅从正数据和未标记 (PU) 数据中训练分类器。然而，现有的 PU 分类方法通常需要准确估计类先验概率，这是一个关键瓶颈，尤其是对于高维数据。这个问题通常通过预先应用主成分分析来解决，但这种无监督的降维会破坏底层的类结构。在这封信中，我们提出了一种基于信息最大化原则的 PU 数据的新型表征学习方法。我们的方法不需要类先验估计，因此可以用作 PU 分类的预处理方法。通过实验，我们证明了我们的方法，结合深度神经网络，