Multi-instance multi-label learning in the presence of novel class instances

TitleMulti-instance multi-label learning in the presence of novel class instances
Publication TypeConference Paper
Year of Publication2015
AuthorsA. T. Pham, R. Raich, X. Z. Fern, and J. Pérez
Conference Name32nd International Conference on Machine Learning (ICML)
Month PublishedJuly
Conference LocationLille, France
AbstractMulti-instance multi-label learning (MIML) is a framework for learning in the presence of label ambiguity. In MIML, experts provide labels for groups of instances (bags), instead of directly providing a label for every instance. When labeling efforts are focused on a set of target classes, instances outside this set will not be appropriately modeled. For example, ornithologists label bird audio recordings with a list of species present. Other additional sound instances, e.g., a rain drop or a moving vehicle sound, are not labeled. The challenge is due to the fact that for a given bag, the presence or absence of novel instances is latent. In this paper, this problem is addressed using a discriminative probabilistic model that accounts for novel instances. We propose an exact and efficient implementation of the maximum likelihood approach to determine the model parameters and consequently learn an instance-level classifier for all classes including the novel class. Experiments on both synthetic and real datasets illustrate the effectiveness of the proposed approach.
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