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

• Main Authors: PANG, Guansong, CAO, Longbing, CHEN, Ling, LIU, Huan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Machine Learning: Data Mining; Machine Learning: Feature Selection/Construction; Databases and Information Systems; Data Storage Systems
• 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
• Institution: Singapore Management University

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...