Conference Agenda
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Statistics in natural sciences and technology Location: 0.001 Session Chair: Gaby Schneider Session Chair: Ansgar Steland | |
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Prior shift estimation for positive unlabeled data through the lens of kernel embedding 1: Warsaw University of Technology, Poland; 2: Institute of Computer Science; 3: Nicolas Copernicus University We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assumed that the source data is partially observable: only samples from the positive class and from the whole population are available (PU learning scenario). We introduce a novel direct estimator of the class prior which avoids estimation of posterior probabilities in both populations and has a simple geometric interpretation. It is based on a distribution matching technique together with kernel embedding in Reproducing Kernel Hilbert Space and is obtained as an explicit solution to an optimisation task. We establish its asymptotic consistency as well as an explicit non-asymptotic bound on its deviation from the unknown prior, which is calculable in practice. We study finite sample behaviour for synthetic and real data and show that the proposal works consistently on par or better than its competitors. | |
