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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

February 1, 2023

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Authors

Jun-Yi Hang

School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China


Min-Ling Zhang

School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China


Yanghe Feng

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China


Xiaocheng Song

Department of Beijing Institute of Electronic Engineering, Beijing 100854, China


DOI:

10.1609/aaai.v36i6.20641


Abstract:

Label-specific features serve as an effective strategy to learn from multi-label data with tailored features accounting for the distinct discriminative properties of each class label. Existing prototype-based label-specific feature transformation approaches work in a three-stage framework, where prototype acquisition, label-specific feature generation and classification model induction are performed independently. Intuitively, this separate framework is suboptimal due to its decoupling nature. In this paper, we make a first attempt towards a unified framework for prototype-based label-specific feature transformation, where the prototypes and the label-specific features are directly optimized for classification. To instantiate it, we propose modelling the prototypes probabilistically by the normalizing flows, which possess adaptive prototypical complexity to fully capture the underlying properties of each class label and allow for scalable stochastic optimization. Then, a label correlation regularized probabilistic latent metric space is constructed via jointly learning the prototypes and the metric-based label-specific features for classification. Comprehensive experiments on 14 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.

Topics: AAAI

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HOW TO CITE:

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification AAAI 2022, 6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song (2022). End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. 2022. End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. (2022) "End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.6847-6855

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song, "End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification", AAAI, p.6847-6855, 2022.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. "End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. "End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6847-6855.

Jun-Yi Hang||Min-Ling Zhang||Yanghe Feng||Xiaocheng Song. End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. AAAI[Internet]. 2022[cited 2023]; 6847-6855.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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