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A Rough Path Theory Approach to Classifying Geometric Paths
http://hdl.handle.net/11316/0002000372
http://hdl.handle.net/11316/00020003721f307aef-269f-48c9-ae78-bf6bde194d3f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文（Article） (424 KB)
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| アイテムタイプ | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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| 公開日 | 2026-03-04 | |||||
| タイトル | ||||||
| タイトル | A Rough Path Theory Approach to Classifying Geometric Paths | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Discriminant Analysis | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Rough Path Theory | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Pattern Recognition | |||||
| 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 | departmental bulletin paper | |||||
| 著者 |
譚, 康融
× 譚, 康融 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This study investigates the effectiveness of Rough Path Theory, specifically path signatures, for classifying geometric patterns, namely, distinguishing circular trajectories from linear or square trajectories. We generate synthetic two-dimensional path datasets consisting of 300 circular trajectories and 300 linear or square trajectories. Each path is embedded into a finite-dimensional feature space using its truncated signature, which captures both local and global geometric information. These signature features are subsequently employed to train a support vector machine (SVM) classifier with a linear kernel. The model achieves 100% classification accuracy on a 70/30 train-test split, with the confusion matrix indicating perfect separation between the two trajectory classes. The results confirm that path signatures encode sufficient information about curvature, ordering, and global topology to discriminate between different geometric trajectories. This demonstrates the potential of signature-based methods for shape recognition and time-series classification, where the geometry of observed paths, rather than raw coordinate values, carries essential diagnostic information. | |||||
| 言語 | en | |||||
| bibliographic_information |
ja : 久留米大学コンピュータジャーナル 巻 40, p. 2-7, 発行日 2026-03 |
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| 出版者 | ||||||
| 出版者 | 久留米大学情報教育センター | |||||
| 言語 | ja | |||||
| item_3_source_id_7 | ||||||
| 収録物識別子タイプ | EISSN | |||||
| 収録物識別子 | 2432-2555 | |||||
| 書誌レコードID（NCID） | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11468134 | |||||
