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  1. NIIテクニカル・レポート

NII Technical Report (NII-2015-002E):Inlierness, Outlierness, Hubness and Discriminability: an Extreme-Value-Theoretic Foundation

https://doi.org/10.20736/0002000327
https://doi.org/10.20736/0002000327
87b5e61b-abc5-479f-bb1f-20f6f46d233a
名前 / ファイル ライセンス アクション
15-002E.pdf NII Technical Report (NII-2015-002E):Inlierness, Outlierness, Hubness and Discriminability: an Extreme-Value-Theoretic Foundation (477 KB)
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-2015-002E):Inlierness, Outlierness, Hubness and Discriminability: an Extreme-Value-Theoretic Foundation
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 10.20736/0002000327
ID登録タイプ JaLC
著者 Houle, Michael E.

× Houle, Michael E.

en Houle, Michael E.

Search repository
抄録
内容記述タイプ Abstract
内容記述 For many large-scale applications in data mining, machine learning, and multimedia, fundamental operations such as similarity search, retrieval, classification, clustering, and anomaly detection generally suffer from an effect known as the `curse of dimensionality'. As the dimensionality of the data increases, distance values tend to become less discriminative, due to their increasing relative concentration about the mean of their distribution. For this reason, researchers have considered the analysis of similarity applications in terms of measures of the intrinsic dimensionality (ID) of the data sets. This theory paper is concerned with a generalization of a discrete measure of ID, the expansion dimension, to the case of continuous distance distributions. This notion of the ID of a distance distribution is shown to precisely coincide with a natural notion of the indiscriminability of distances, thereby establishing a theoretically-founded relationship among probability density, the cumulative density (cumulative probability divided by distance), intrinsic dimensionality, and discriminability. The indiscriminability function proposed in this paper is shown to completely determine an extreme-value-theoretic representation of the distance distribution. From this representation, a characterization in terms of continuous ID is derived for the notions of outlierness and inlierness of data, as well as the hubness phenomenon in data sets.
言語 en
書誌情報 ja : NIIテクニカル・レポート
en : NII Technical Report

p. 1-32, 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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