单分类（one-class classification）是针对只有正样本的数据集所使用的分类方法。它试图从数据集寻找模式，因而从更大范围的假设空间中有效地将正样本和潜在的负样本分开。单分类算法只关注与样本的相似或匹配程度，对于未知的部分不妄下结论。
References
In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class,[1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary. This is different from and more difficult than the traditional classification) problem, which tries to distinguish between two or more classes with the training set containing objects from all the classes. An example is the classification of the operational status of a nuclear plant as ‘normal’:[2] In this scenario, there are few, if any, examples of catastrophic system states; only the statistics of normal operation are known.
While many of the above approaches focus on the case of removing a small number of outliers or anomalies, one can also learn the other extreme, where the single class covers a small coherent subset of the data, using an information bottleneck approach.[3]
TensorFlow上实践基于自编码的One Class Learning
相关Paper：
Learning Deep Features for One-Class Classification
A Survey of Recent Trends in One Class Classification
One-class classification: taxonomy of study and review of techniques
One-Class SVMs for Document Classification
One-class document classification via Neural Networks
One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes
Github 实现：