Fast Multi-View Outlier Detection via Deep Encoder

Abstract

Multi-view outlier detection has a wide range of applications and has been well investigated in recent years. However, 1) most existing state-of-the-art methods cannot efficiently handle outlier detection problem for large-scale multi-view data, since exploring pairwise constraints among different views causes highly-computational cost; 2) the data collected from original heterogeneous feature spaces further increases the consistent difficulty of multi-view outlier detection. To address these issues, we present a fast multi-view outlier detection model via learning a low-rank latent subspace representation with deep encoder architecture, which can not only efficiently identify the outliers for large-scale data even with numerous data views, but also exploit a discriminative common latent subspace shared by all the views. First, we learn a set of orthogonal bases as view-specific dictionaries from a small dataset, which is randomly sampled from the original dataset. Benefitting from view-specific dictionaries, the sampled data is projected and decomposed as a shared and discriminative latent subspace representations, which correspond to the view-consistent and view-specific components across multiple views, respectively. Then, the obtained discriminative latent representations are applied to train the view-specific deep encoders, which can efficiently compute the abnormal score for the remaining instances. Our proposed model can cost-effectively identify the outliers in large-scale datasets from numerous data views with less computational complexity. Experiments conducted on eight real datasets and a synthesis dataset show that our proposed model outperforms the existing ones on effectiveness and efficiency.