Multi-view dimensionality reduction based on Universum learning

Abstract

Abstract Canonical correlation analysis (CCA) is a useful dimensionality reduction method and has been widely used in projecting multi-view data. However, CCA learns from training data consisting of only target classes, ignoring Universum data that belongs to none of the target classes but comes from the same domain as the target classes. Recently, incorporating Universum data in learning has been used to gain more prior knowledge about the application domain and has been shown to achieve favorable improvements. In this paper, we extend CCA with Universum learning for multi-view data and the proposed method is termed as Universum CCA (UCCA). Due to the fact that Universum data in each view does not belong to any target class, correlation between Universum data and target data should be minimized. Consequently, UCCA aims to find basis vectors in multiple views to ensure that correlations between projections of target data are mutually maximized but correlations between projections of Universum data and target data mutually minimized. UCCA can be expressed as a generalized eigenvalue problem and the extracted features express patterns more distinctly. The experimental results on several real-world datasets demonstrate its marked improvements over conventional methods.