Multiview Multi-Instance Multilabel Active Learning

doi:10.1109/TNNLS.2021.3056436

Multiview Multi-Instance Multilabel Active Learning

2022-09-01

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AI

DOI：10.1109/TNNLS.2021.3056436

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Multiview multi-instance multilabel learning (M3L) is a framework for modeling black complex objects. In this framework, each object (or bag) contains one or more instances, is represented with different feature views, and simultaneously annotated with a set of nonexclusive semantic labels. Given the multiplicity of the studied objects, traditional M3L methods generally demand a large number of labeled bags to train a predictive model to annotate bags (or instances) with semantic labels. However, annotating sufficient bags is very expensive and often impractical. In this article, we present an active learning-based M3L approach (M3AL) to reduce the labeling costs of bags and to improve the performance as much as possible. M3AL first adapts the multiview self-representation learning to evacuate the shared and individual information of bags and to learn the shared/individual similarities between bags across/within views. Next, to avoid scrutinizing all the possible labels, M3AL introduces a new query strategy that leverages the shared and individual information, and the diverse instance distribution of bags across views, to select the most informative bag-label pair for the query. Experimental studies on benchmark data sets show that M3AL can significantly reduce the query costs while achieving a better performance than other related competitive methods at the same cost.

Keywords:

Drugs

Biological systems

Correlation

Compounds

Uncertainty

Semantics

Learning systems

Active learning

commonality and individuality

multi-instance multilabel (MIML) learning

multiview learning

Multiview Multi-Instance Multilabel Active Learning

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