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Simultaneous Visualization of Documents, Words and Topics by Tensor Self-Organizing Map and Non-negative Matrix Factorization
http://hdl.handle.net/10228/00008362
http://hdl.handle.net/10228/00008362eced3bf4-8bf9-4162-abdf-89f84d48c822
名前 / ファイル | ライセンス | アクション |
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neuro_22.pdf (3.2 MB)
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||
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公開日 | 2021-06-10 | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
タイトル | ||||||||||||
タイトル | Simultaneous Visualization of Documents, Words and Topics by Tensor Self-Organizing Map and Non-negative Matrix Factorization | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
著者 |
Noguchi, Kazuki
× Noguchi, Kazuki× Ishida, Takuro× 古川, 徹生
WEKO
661
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | The purpose of this work is to develop a simultaneous visualization method of documents, words, and topics. The task of the proposed method is to map a set of documents to a pair of low-dimensional latent spaces corresponding to documents and words, by which the relations between them are visualized. In addition, the method also decomposes the mapping as the sum of topics, so that the topic distributions are visualized on the latent spaces. To achieve the task, we combined the tensor self-organizing map and the non-negative matrix factorization. We applied the method to NeurIPS data set, and the result shows that the method enables us to understand the tripartite relation between document, words and topics easily. | |||||||||||
備考 | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS), December 5-8, 2020, Hachijo island, Tokyo, Japan(オンライン開催に変更) | |||||||||||
書誌情報 |
2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS) 発行日 2021-01-21 |
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出版社 | ||||||||||||
出版者 | IEEE | |||||||||||
DOI | ||||||||||||
関連タイプ | isVersionOf | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.1109/SCISISIS50064.2020.9322683 | |||||||||||
ISBN | ||||||||||||
識別子タイプ | ISBN | |||||||||||
関連識別子 | 978-1-7281-9732-6 | |||||||||||
ISBN | ||||||||||||
識別子タイプ | ISBN | |||||||||||
関連識別子 | 978-1-7281-9733-3 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | tensor self-organizing map | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | document analysis | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | topic | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | non-negative matrix factorization | |||||||||||
出版タイプ | ||||||||||||
出版タイプ | AM | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||
査読の有無 | ||||||||||||
値 | yes | |||||||||||
連携ID | ||||||||||||
8966 |