论文标题
通过张量网络进行积极的未标记学习
Positive unlabeled learning with tensor networks
论文作者
论文摘要
积极的未标记学习是一个二进制分类问题，具有正面和未标记的数据。它在负面标签昂贵或无法获得的域中很常见，例如医学和个性化广告。积极未标记学习的大多数方法适用于特定数据类型（例如，图像，分类数据），无法生成新的正面和负样本。这项工作引入了一种基于功能空间的张量张量网络方法，以解决积极的未标记学习问题。提出的方法不是特定领域的，并且可以显着改善MNIST图像和15个分类/混合数据集的最新结果。训练有素的张量网络模型也是一种生成模型，可以生成新的正面和负面实例。
Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new positive and negative samples. This work introduces a feature-space distance-based tensor network approach to the positive unlabeled learning problem. The presented method is not domain specific and significantly improves the state-of-the-art results on the MNIST image and 15 categorical/mixed datasets. The trained tensor network model is also a generative model and enables the generation of new positive and negative instances.
