MVOD: A Multi-View Outlier Detection Method with Single-Feature View Augmentation

Outlier detection identifies rare items, events, or observations in data analysis and has critical applications in many fields. In most such applications, datasets are high-dimensional. To reduce the impact of the “curse of dimension-ality”, many such applications decompose the entire feature space into different subspaces with two or more “relevant” features for deviations of interest. Those approaches often ignore the case for subspaces with a single feature. Due to the low dimension and high data density, it sometimes sufficient to identify univariate outliers. Thus, this paper proposes a multi-view outlier detection algorithm MVOD to ensemble outlier detection from three views: single feature view, local view, and global view. More specifically, we design a general outlier score function based on the quantities of information to evaluate the strength of the data distribution structure. Then, the outlier score for each point from different views is normalized and combined to reduce representational bias under different views. Extensive experiments are carried out on ten public benchmark datasets with ten state-of-art baselines. Experimental results show that MVOD is significantly better than those baselines in terms of AVC_ROC.