Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly...

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Bibliographic Details
Main Authors: PANG, Guansong, CAO, Longbing, CHEN, Ling, LIU, Huan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/7144
https://ink.library.smu.edu.sg/context/sis_research/article/8147/viewcontent/0360.pdf
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Institution: Singapore Management University
Language: English
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