Anh Pham,
Raviv Raich,
Xiaoli Fern,
Jesús Pérez Arriaga
;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2427-2435, 2015.
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.
@InProceedings{pmlr-v37-pham15,
title = {Multi-instance multi-label learning in the presence of novel class instances},
author = {Anh Pham and Raviv Raich and Xiaoli Fern and Jesús Pérez Arriaga},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {2427--2435},
year = {2015},
editor = {Francis Bach and David Blei},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/pham15.pdf},
url = {http://proceedings.mlr.press/v37/pham15.html},
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.}
}
%0 Conference Paper
%T Multi-instance multi-label learning in the presence of novel class instances
%A Anh Pham
%A Raviv Raich
%A Xiaoli Fern
%A Jesús Pérez Arriaga
%B Proceedings of the 32nd International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2015
%E Francis Bach
%E David Blei
%F pmlr-v37-pham15
%I PMLR
%J Proceedings of Machine Learning Research
%P 2427--2435
%U http://proceedings.mlr.press
%V 37
%W PMLR
%X 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.
TY - CPAPER
TI - Multi-instance multi-label learning in the presence of novel class instances
AU - Anh Pham
AU - Raviv Raich
AU - Xiaoli Fern
AU - Jesús Pérez Arriaga
BT - Proceedings of the 32nd International Conference on Machine Learning
PY - 2015/06/01
DA - 2015/06/01
ED - Francis Bach
ED - David Blei
ID - pmlr-v37-pham15
PB - PMLR
SP - 2427
DP - PMLR
EP - 2435
L1 - http://proceedings.mlr.press/v37/pham15.pdf
UR - http://proceedings.mlr.press/v37/pham15.html
AB - 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.
ER -
Pham, A., Raich, R., Fern, X. & Arriaga, J.P.. (2015). Multi-instance multi-label learning in the presence of novel class instances. Proceedings of the 32nd International Conference on Machine Learning, in PMLR 37:2427-2435
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