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アイテム
Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data
http://hdl.handle.net/10061/0002001152
http://hdl.handle.net/10061/0002001152e98bc761-864a-4086-b274-88cef6c8306f
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2025-09-22 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Matrix decomposition | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Tensors | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Public transportation | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Signal processing | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Vectors | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Symmetric matrices | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Feature extraction | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Codes | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Benchmark testing | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Singular value decomposition | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Graph analysis | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | mobility data | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | nonnegative matrix factorization | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Wasserstein distance | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ | journal article | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 著者 |
Kaji, Hirotaka
× Kaji, Hirotaka
× 池田, 和司
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal processing methods such as singular value decomposition and tensor analysis, which can estimate missing values. However, their feature extraction abilities are insufficient for optimizing mobility networks. This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is defined as the dual graph of a graph, where the vertices correspond to the edges of the original graph, and the edges correspond to the vertices. Thus, the shortest path length between two vertices in the line graph corresponds to the distance between the edges in the original graph. Through experiments using synthetic and benchmark datasets, we show that the performance and robustness of our method are superior to conventional methods. Additionally, we apply our method to real-world taxi origin—destination data as a mobility dataset and discuss the findings. | |||||||||||||
| 書誌情報 |
en : IEEE Open Journal of Signal Processing 巻 6, p. 194-202, ページ数 9, 発行日 2025-01-14 |
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| 出版者 | IEEE | |||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2644-1322 | |||||||||||||
| 出版者版DOI | ||||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/OJSP.2025.3528869 | |||||||||||||
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| 関連タイプ | isReplacedBy | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
| 関連識別子 | https://ieeexplore.ieee.org/abstract/document/10840315 | |||||||||||||
| 権利 | ||||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||
| 権利情報 | © 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||
| 著者版フラグ | ||||||||||||||
| 出版タイプ | NA | |||||||||||||
