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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 44; H. 3; S. 1320 - 1337
Hauptverfasser:	Nguyen, Tam, Raich, Raviv
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0162-8828 1939-3539 2160-9292 1939-3539
DOI:	10.1109/TPAMI.2020.3017456