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  1. NIIテクニカル・レポート

NII Technical Report (NII-2015-003E):Intrinsic Dimensional Outlier Detection in High-Dimensional Data

https://doi.org/10.20736/0002000328
https://doi.org/10.20736/0002000328
84c29f8e-82ea-429f-8733-4cd3b3e88968
名前 / ファイル ライセンス アクション
15-003E.pdf NII Technical Report (NII-2015-003E):Intrinsic Dimensional Outlier Detection in High-Dimensional Data (3.7 MB)
Item type レポート / Report(1)
公開日 2022-06-08
タイトル
言語 en
タイトル NII Technical Report (NII-2015-003E):Intrinsic Dimensional Outlier Detection in High-Dimensional Data
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

en Brünken, Jonathan von

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Houle, Michael E.

× Houle, Michael E.

en Houle, Michael E.

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Zimek, Arthur

× Zimek, Arthur

en 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
出版者
言語 ja
出版者 国立情報学研究所
ISSN
収録物識別子タイプ ISSN
収録物識別子 1346-5597
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