| 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-2016-004E)：Measuring Dependency via Intrinsic Dimensionality |
|
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 |
10.20736/0002000336 |
|
ID登録タイプ |
JaLC |
| 著者 |
Romano, Simone
Chelly, Oussama
Nguyen, Vinh
Bailey, James
Houle, Michael E.
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Measuring the amount of dependency among multiple variables is an important task in pattern recognition. In the last few years, many new dependency measures have been developed for the exploration of functional relationships. In this paper, we develop a dependency measure between variables based on an extreme-value theoretic treatment of intrinsic dimensionality. Our measure identifies variables with low intrinsic dimension — that is, those that support embeddings of the data within low-dimensional manifolds. To build a dependency measure on strong foundations, we theoretically prove a connection between information theory and intrinsic dimensionality theory. This allows us also to propose novel estimators of intrinsic dimensionality. Finally, we show that our dependency measure enables to find patterns that cannot be found by other state-of-the-art measures on real and synthetic data. |
|
言語 |
en |
| 書誌情報 |
ja : NIIテクニカル・レポート
en : NII Technical Report
p. none,
発行日 2016-06-06
|
| 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 |
NII Technical Report (NII-2016-004E)：Measuring Dependency via Intrinsic Dimensionality (650 KB)
