Efficient Training for Positive Unlabeled Learning

TL;DR

This paper introduces a scalable positive unlabeled learning algorithm that is both theoretically optimal and computationally efficient, suitable for large datasets and practical applications.

Contribution

A novel PU learning algorithm that ensures optimal solutions with improved scalability and resource efficiency compared to existing methods.

Findings

Proposed method is theoretically proven to be optimal.

Algorithm demonstrates superior computational and memory performance.

Effective on various real-world PU learning tasks.

Abstract

Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.