| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-08-18 |
| タイトル |
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タイトル |
Multi-label Learning with Random Circular Vectors |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
conference paper |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Nishida, Ken
Machi, Kojiro
Onishi, Kazuma
Hayashi, Katsuhiko
上垣外, 英剛
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
The extreme multi-label classification (XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks (DNNs) have demonstrated remarkable success in XMC problems, the task is still challenging because it must deal with a large number of output labels, which make the DNN training computationally expensive. This paper addresses the issue by exploring the use of random circular vectors, where each vector component is represented as a complex amplitude. In our framework, we can develop an output layer and loss function of DNNs for XMC by representing the final output layer as a fully connected layer that directly predicts a low-dimensional circular vector encoding a set of labels for a data instance. We conducted experiments on synthetic datasets to verify that circular vectors have better label encoding capacity and retrieval ability than normal real-valued vectors. Then, we conducted experiments on actual XMC datasets and found that these appealing properties of circular vectors contribute to significant improvements in task performance compared with a previous model using random real-valued vectors, while reducing the size of the output layers by up to 99%. |
| 書誌情報 |
en : Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)
p. 245-255,
ページ数 11,
発行日 2024-08-15
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| 会議情報 |
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会議名 |
The 9th Workshop on Representation Learning for NLP (RepL4NLP-2024) |
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開始年 |
2024 |
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開始月 |
08 |
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開始日 |
15 |
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終了年 |
2024 |
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終了月 |
08 |
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終了日 |
15 |
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開催期間 |
2024-08-15 - 2024-08-15 |
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開催地 |
Bangkok, Thailand |
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開催国 |
THA |
| 出版者 |
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出版者 |
Association for Computational Linguistics |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://aclanthology.org/2024.repl4nlp-1.18/ |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A92024 Association for Computational Linguistics. ACL materials are Copyright $00A9 1963$20132025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| 著者版フラグ |
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出版タイプ |
NA |
