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We propose a new problem formulation which is similar to, but moreinformative than, the binary multiple-instance learning problem. In thissetting, we are given groups of instances (described by feature vectors) alongwith estimates of the fraction of positively-labeled instances per group. Thetask is to learn an instance level classifier from this information. That is,we are trying to estimate the unknown binary labels of individuals fromknowledge of group statistics. We propose a principled probabilistic model tosolve this problem that accounts for uncertainty in the parameters and in theunknown individual labels. This model is trained with an efficient MCMCalgorithm. Its performance is demonstrated on both synthetic and real-worlddata arising in general object recognition.

Learning about individuals from group statistics