Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup, and show how these classification tasks can usefully be analyzed as weakly supervised clustering problems. We propose three approaches to solving the weakly supervised clustering problem, including a latent variables model that performs well in our experiments. We illustrate our methods on an industry dataset that was the original motivation for this research.
Similar to the above work on learning from labeled proportions, REF considers a classification problem with no access to labels for individual training examples, but only average labels over subpopulations.
Weakly Supervised Clustering: Learning Fine-Grained Signals from Coarse Labels
Annals of Applied Statistics 2015, Vol. 9, No. 2, 801-820
Computer Science Mathematics