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

Abstract: Multi-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.

 Congreso: International Conference on Machine Learning (32ª : 2015 : Lille)

 Editorial: JMLR ; Microtome Publishing

 Año de publicación: 2015

 Nº de páginas: 9

 Tipo de publicación: Comunicación a Congreso

 ISSN: 1938-7288

 Url de la publicación: http://www.jmlr.org/proceedings/papers/v37/pham15.html

Autoría

PHAM, ANH T.

RAICH, RAVIV

FERN, XIAOLI Z.