This work contributes towards the development of a predictive model for the classification of multi-view data. In the proposed approach, the relationship between each pair of views is assumed to be unique. Hence, a loss function is proposed to efficiently capture the cross-view dependency across several views. It extracts the relevant cross-view information in terms of consensus and/or complementary knowledge from the input pairs of views. Instead of heuristically determining the architecture of the proposed deep model, an optimal architecture is estimated for each given database based on the Bayes error analysis of the network. While the number of layers is estimated from the total error probability of the model, the number of nodes at each layer is computed based on the Hilbert-Schmidt independence criterion. The proposed model outperforms state-of-the-art algorithms in 81.82% cases, considering five benchmark and three omics databases. In case of omics data, where the number of samp...

In recent years, a surging interest is noted for combining the information of multiple views to obtain a joint representation of the given data. In multi-view data analysis, the joint representation should be learned from the given input views in such a way that the view-specific information as well as the cross-view dependency are preserved properly. In the context of cross-view dependency, it is expected that both view-consistency and view-discrepancy are addressed simultaneously. Discriminability of the joint representation is also an important aspect in the classification problem. In this regard, a novel deep learning model is proposed to efficiently encapsulate the underlying data distribution over the space of input views. Considering both consensus and complementary principles, a loss function is introduced, based on the concept of the Hilbert–Schmidt independence criterion, to capture the relevant cross-view information from the given multi-view data. Incorporating the supervis...