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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Main Authors: | , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2017
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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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