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NII Technical Report (NII-2015-003E)：Intrinsic Dimensional Outlier Detection in High-Dimensional Data
https://doi.org/10.20736/0002000328
https://doi.org/10.20736/000200032884c29f8e-82ea-429f-8733-4cd3b3e88968
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
NII Technical Report (NII-2015-003E)：Intrinsic Dimensional Outlier Detection in High-Dimensional Data (3.7 MB)
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| Item type | レポート / Report(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2022-06-08 | |||||||||||
| タイトル | ||||||||||||
| タイトル | NII Technical Report (NII-2015-003E)：Intrinsic Dimensional Outlier Detection in High-Dimensional Data | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 言語 | ja | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | テクニカルレポート | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Technical Report | |||||||||||
| 資源タイプ | ||||||||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| タイプ | departmental bulletin paper | |||||||||||
| ID登録 | ||||||||||||
| ID登録 | 10.20736/0002000328 | |||||||||||
| ID登録タイプ | JaLC | |||||||||||
| 著者 |
Brünken, Jonathan von
× Brünken, Jonathan von
× Houle, Michael E.
× Zimek, Arthur
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | We introduce a new method for evaluating local outliers, by utilizing a measure of the intrinsic dimensionality in the vicinity of a test point, the continuous intrinsic dimension (ID), which has been shown to be equivalent to a measure of the discriminative power of similarity functions. Continuous ID can be regarded as an extension of Karger and Ruhl's expansion dimension to a statistical setting in which the distribution of distances to a query point is modeled in terms of a continuous random variable. The proposed local outlier score, IDOS, uses ID as a substitute for the density estimation used in classical outlier detection methods such as LOF. An experimental analysis is provided showing that the precision of IDOS substantially improves over that of state-of-the-art outlier detection scoring methods, especially when the data sets are large and high-dimensional. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
ja : NIIテクニカル・レポート en : NII Technical Report p. 1-12, 発行日 2015-03-31 |
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| 出版者 | ||||||||||||
| 出版者 | 国立情報学研究所 | |||||||||||
| 言語 | ja | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 1346-5597 | |||||||||||
