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Information Mandala : Statistical Distance Matrix with Clustering
https://iwate-u.repo.nii.ac.jp/records/15757
https://iwate-u.repo.nii.ac.jp/records/15757e0175160-8881-4d1f-843f-04083a16ddcd
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
ieeea-v9p56563-56577 (6.8 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2022-08-04 | |||||||
| タイトル | ||||||||
| タイトル | Information Mandala : Statistical Distance Matrix with Clustering | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | Statistical distance matrix | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | hierarchical clustering | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | Mandala | |||||||
| 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 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| 著者 |
LU, Xin
× LU, Xin （著）
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| 登録日 | ||||||||
| 日付 | 2022-08-04 | |||||||
| 日付タイプ | Created | |||||||
| 書誌情報 |
en : IEEE Access 巻 9, p. 56563-56577, ページ数 15, 発行日 2021-04-09 |
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| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2169-3536 | |||||||
| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | In machine learning, observation features are measured in a metric space to obtain their distance function for optimization. Given similar features that are statistically sufficient as a population, a statistical distance between two probability distributions can be calculated for more precise learning. Provided the observed features are multi-valued, the statistical distance function is still efficient. However, due to its scalar output, it cannot be applied to represent detailed distances between feature elements. To resolve this problem, this paper extends the traditional statistical distance to a matrix form, called a statistical distance matrix. The proposed approach performs well in object recognition tasks and clearly and intuitively represents the dissimilarities between cat and dog images in the CIFAR dataset, even when directly calculated using the image pixels. By using the hierarchical clustering of the statistical distance matrix, the image pixels can be separated into several clusters that are geometrically arranged around a center like a Mandala pattern. The statistical distance matrix with clustering is called the Information Mandala. | |||||||
| 言語 | en | |||||||
| 抄録（URL） | ||||||||
| 言語 | en | |||||||
| 表示名 | Information Mandala : Statistical Distance Matrix With Clustering | |||||||
| URL | https://ieeexplore.ieee.org/document/9399434 | |||||||
| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| 言語 | en | |||||||
| 権利 | ||||||||
| 権利情報 | @2021 by the author | |||||||
| 言語 | en | |||||||
| 権利 | ||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||
| 権利情報 | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | |||||||
| 言語 | en | |||||||
| DOI | ||||||||
| 関連タイプ | isIdenticalTo | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2021.3072237 | |||||||
| 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 | |||||||
