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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Compact Multi-Label Learning

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Xiaobo Shen

Nanyang Technological University


Weiwei Liu

The University of New South Wales


Ivor Tsang

University of Technology Sydney


Quan-Sen Sun

Nanjing University of Science and Technology


Yew-Soon Ong

Nanyang Technological University


DOI:

10.1609/aaai.v32i1.11708


Abstract:

Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance. Besides, they suffer from expensive prediction computational costs when applied to large-scale datasets. To address the above issues, this paper proposes a Co-Hashing (CoH) method by formulating multi-label learning from the perspective of cross-view learning. CoH first regards the input and output as two views, and then aims to learn a common latent hamming space, where input and output pairs are compressed into compact binary embeddings. CoH enjoys two key benefits: 1) the input and output can be well aligned, and their correlations are explored; 2) the prediction is very efficient using fast cross-view kNN search in the hamming space. Moreover, we provide the generalization error bound for our method. Extensive experiments on eight real-world datasets demonstrate the superiority of the proposed CoH over the state-of-the-art methods in terms of both prediction accuracy and efficiency.

Topics: AAAI

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HOW TO CITE:

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong Compact Multi-Label Learning Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong Compact Multi-Label Learning AAAI 2018, .

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong (2018). Compact Multi-Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. Compact Multi-Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. 2018. Compact Multi-Label Learning. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. (2018) "Compact Multi-Label Learning", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong, "Compact Multi-Label Learning", AAAI, p., 2018.

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. "Compact Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. "Compact Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Xiaobo Shen||Weiwei Liu||Ivor Tsang||Quan-Sen Sun||Yew-Soon Ong. Compact Multi-Label Learning. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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