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Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao

Research Collection School Of Computing and Information Systems

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 full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective …