Multi-Class Classification from Single-Class Data with Confidences
This addresses a data scarcity issue in machine learning by enabling multi-class classification from limited single-class data, though it is incremental as it builds on existing confidence-based methods.
The paper tackles the problem of learning a multi-class classifier using only data from a single class, by leveraging confidences (class-posterior probabilities) to achieve discriminative classification across all classes without needing data from other classes, and demonstrates effectiveness with experimental results.
Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i.e., the class-posterior probabilities for all the classes) are available. Specifically, we propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. Instead of constructing a boundary between the given class and other classes, our method can conduct discriminative classification between all the classes even if no data from the other classes are provided. We further theoretically and experimentally show that our method can be Bayes-consistent with a simple modification even if the provided confidences are highly noisy. Then, we provide an extension of our method for the case where data from a subset of all the classes are available. Experimental results demonstrate the effectiveness of our methods.