Multi-instance multi-label learning in the presence of novel class instances
| Title | Multi-instance multi-label learning in the presence of novel class instances |
| Publication Type | Conference Paper |
| Year of Publication | 2015 |
| Authors | Pham, A. T., R. Raich, X. Z. Fern, and J. Pérez |
| Conference Name | 32nd International Conference on Machine Learning (ICML) |
| Month Published | July |
| Conference Location | Lille, France |
| 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. |
| URL | http://icml.cc/2015/?page_id=825#SupervisedLearning I |
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