WEKO3
アイテム
Revisiting a Nearest Neighbor Method for Shape Classification
https://hiroshima-cu.repo.nii.ac.jp/records/1821
https://hiroshima-cu.repo.nii.ac.jp/records/1821bc6e4254-cd82-4219-8389-f54cf8cd280a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
e103-d_12_2649.pdf (608.4 kB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2023-03-07 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Revisiting a Nearest Neighbor Method for Shape Classification | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題 | shape classification | |||||||||||
| キーワード | ||||||||||||
| 主題 | ordinary Procrustes sum of squares | |||||||||||
| キーワード | ||||||||||||
| 主題 | nearest neighbor method | |||||||||||
| キーワード | ||||||||||||
| 主題 | discriminant adaptive nearest neighbor method | |||||||||||
| 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 | |||||||||||
| 著者 |
IWATA, Kazunori
× IWATA, Kazunori
× 岩田, 一貴
|
|||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | The nearest neighbor method is a simple and flexiblescheme for the classification of data points in a vector space. It predictsa class label of an unseen data point using a majority rule for the labels ofknown data points inside a neighborhood of the unseen data point. Becauseit sometimes achieves good performance even for complicated problems,several derivatives of it have been studied. Among them, the discriminantadaptive nearest neighbor method is particularly worth revisiting to demon-strate its application. The main idea of this method is to adjust the neigh-bor metric of an unseen data point to the set of known data points beforelabel prediction. It often improves the prediction, provided the neighbormetric is adjusted well. For statistical shape analysis, shape classificationattracts attention because it is a vital topic in shape analysis. However, be-cause a shape is generally expressed as a matrix, it is non-trivial to applythe discriminant adaptive nearest neighbor method to shape classification.Thus, in this study, we develop the discriminant adaptive nearest neighbormethod to make it slightly more useful in shape classification. To achievethis development, a mixture model and optimization algorithm for shapeclustering are incorporated into the method. Furthermore, we describe sev-eral helpful techniques for the initial guess of the model parameters in theoptimization algorithm. Using several shape datasets, we demonstrated thatour method is successful for shape classification. | |||||||||||
| 書誌情報 |
IEICE Transactions on Information and Systems 巻 E103-D, 号 12, p. 2649-2658, 発行日 2020-12-01 |
|||||||||||
| 出版者 | ||||||||||||
| 出版者 | 電子情報通信学会 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 09168532|17451361 | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA10826272|AA11226532|AA11510321 | |||||||||||
| DOI | ||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | info:doi/https://doi.org/10.1587/transinf.2020EDP7074 | |||||||||||
| 権利 | ||||||||||||
| 権利情報 | Copyright©2020 The Institute of Electronics, Information and Communication Engineers | |||||||||||
| 関連サイト | ||||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://search.ieice.org/ | |||||||||||
| 関連名称 | https://search.ieice.org/ | |||||||||||
| 他の資源との関係 | ||||||||||||
| 関連名称 | https://search.ieice.org/bin/summary.php?id=e103-d_12_2649&category=D&year=2020&lang=E&abst= | |||||||||||
| フォーマット | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | application/pdf | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
