Incomplete Label Multiple Instance Multiple Label Learning

With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 3; pp. 1320 - 1337
Main Authors: Nguyen, Tam, Raich, Raviv
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Birds
Cognitive tasks
EM algorithm
Graphical models
Incomplete-label learning
Inference
Labeling
Labels
learning with missing labels
marginal maximum likelihood
maximum likelihood
Models, Statistical
multi-instance multi-label learning
multiple-instance multiple-label learning
Numerical models
Phase locked loops
Probabilistic models
Robustness (mathematics)
Semi-supervised learning
Supervised Machine Learning
Training
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label learning, multiple-instance learning, and active learning. Here, we focus on multiple-instance multiple-label learning with missing bag labels. Little research has been done for this challenging yet potentially powerful variant of incomplete supervision learning. We introduce a novel discriminative probabilistic model for missing labels in multiple-instance multiple-label learning. To address inference challenges, we introduce an efficient implementation of the EM algorithm for the model. Additionally, we consider an alternative inference approach that relies on maximizing the label-wise marginal likelihood of the proposed model instead of the joint likelihood. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach. In particular, comparison to state-of-the-art methods shows that our approach introduces a significantly smaller decrease in performance when the proportion of missing labels is increased.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3017456