Learning From PU Data Using Disentangled Representations.

We address the problem of learning a binary classifier given partially labeled data where all labeled samples come from only one of the classes, commonly known as Positive Unlabeled (PU) learning. Classical methods such as clustering, out-of-distribution detection, and positive density estimation, while effective in low-dimensional scenarios, lose their efficacy as the dimensionality of data increases, because of the increasing complexity. This has led to the development of methods that address the problem in high-dimensional spaces; however, many of these methods are also impacted by the increased complexity inherent in high-dimensional data. The contribution of this paper is the learning of a neural network-based data representation by employing a loss function that enables the projection of unlabeled data into two distinct clusters - positive and negative - facilitating their identification through basic clustering techniques and mirroring the simplicity of the problem seen in low-dimensional settings. We further enhance this separation of unlabeled data clusters by implementing a vector quantization strategy. Our experimental results on benchmarking PU datasets validate the superiority of our method over existing state-of-the-art techniques. Additionally, we provide theoretical justification to support our cluster-based approach and algorithmic choices.