Latent Discriminant Subspace Representations for Multi-View Outlier Detection

Identifying multi-view outliers is challenging because of the complex data distributions across different views. Existing methods cope this problem by exploiting pairwise constraints across different views to obtain new feature representations,based on which certain outlier score measurements are defined. Due to the use of pairwise constraint, it is complicated and time-consuming for existing methods to detect outliers from three or more views. In this paper, we propose a novel method capable of detecting outliers from any number of dataviews. Our method first learns latent discriminant representations for all view data and defines a novel outlier score function based on the latent discriminant representations. Specifically, we represent multi-view data by a global low-rank representation shared by all views and residual representations specific to each view. Through analyzing the view-specific residual representations of all views, we can get the outlier score for every sample. Moreover, we raise the problem of detectinga third type of multi-view outliers which are neglected by existing methods. Experiments on six datasets show our method outperforms the existing ones in identifying all types of multi-view outliers, often by large margins.