Understanding partial multi-label learning via mutual information
AUTHORs: 
Xiuwen Gong, Dong Yuan, Wei BaoAuthors Info & Claims
Xiuwen Gong, Dong Yuan, Wei BaoAuthors Info & ClaimsArticle No.: 317, Pages 4147 - 4156
Abstract
To deal with ambiguities in partial multi-label learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly. However, there is an essential question: "Can the ground-truth labels be identified precisely?". If yes, "How can the ground-truth labels be found?". This paper provides affirmative answers to these questions. Instead of adopting hand-made heuristic strategy, we propose a novel Mutual Information Label Identification for Partial Multi-Label Learning (MILI-PML), which is derived from a clear probabilistic formulation and could be easily interpreted theoretically from the mutual information perspective, as well as naturally incorporates the feature/label relevancy into consideration. Extensive experiments on synthetic and real-world datasets clearly demonstrate the superiorities of the proposed MILI-PML.
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Published In
December 2021
30517 pages
ISBN:9781713845393
Copyright © 2021 Neural Information Processing Systems Foundation, Inc.
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Curran Associates Inc.
Red Hook, NY, United States
Publication History
Published: 10 June 2024
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