Class-structure preserving multi-view correlated discriminant analysis for multiblock data

With the rapid development in data acquisition methods, multiple data sources are now becoming available to explain different views of an object. This consequently introduces several new challenges in integrating the high dimensional, distinct, and heterogeneous views under multi-view learning (MVL) framework. The multiset canonical correlation analysis (MCCA) is a popular subspace learning technique in MVL, which forms a common latent space by maximizing the pairwise correlation across all the views. However, MCCA does not utilize the class label information of the objects and is unable to handle the data non-linearity. Although there exist a few supervised extensions of MCCA, they lack productive use of intra-view and inter-view consistency and/or inconsistency information while using the class label. In this regard, a supervised subspace learning method, termed as class-structure preserving multi-view correlated discriminant analysis (CSP-MvCDA), is proposed by judiciously integrating the merits of MCCA, linear discriminant analysis (LDA), and a locality preserving norm. The proposed method jointly optimizes the inter-set correlation across all the views and intra-set discrimination in each view to obtain a common discriminative latent space, where the shared and complementary information across multiple views is exploited. The locality preserving norm with prior class labels helps to preserve the local class-structure of the data, while the LDA maintains its global class-structure. To show the effectiveness of the proposed method, several cancer and benchmark data sets are used. The experimental results establish that the proposed CSP-MvCDA method is superior to several state-of-the-art algorithms in terms of classification performance.