Selective value coupling learning for detecting outliers in high-dimensional categorical data

Selective value coupling learning for detecting outliers in high-dimensional categorical data

This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a f...

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書目詳細資料
Main Authors:	PANG, Guansong, XU, Hongzuo, CAO Longbing, ZHAO, Wentao
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2017
主題:	Outlier Detection High-Dimensional Data Categorical Data Feature Selection Coupling Learning Databases and Information Systems Data Storage Systems
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/7142 https://ink.library.smu.edu.sg/context/sis_research/article/8145/viewcontent/3132847.3132994.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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