Robust Diverse Multi-View Learning for Cancer Subtyping

Cancer subtyping is crucial for categorizing patients into distinct groups, enabling precision medicine and personalized therapies. As multi-omic analysis becomes more prevalent, integrating data from various omics provides deeper insights into the potential relationships between cancer subtypes. Although most cancer subtyping methods show promising performance, they have several limitations. These methods fail to account for omic differences, adequately address noise in similarity matrices, and preserve the manifold structure of high-dimensional data in the low-dimensional space. This study proposes a Robust Diverse Multi-view Learning (RDML) model for cancer subtyping. Specifically, multi-view self-representation matrices are formulated as a third-order tensor. Differences between views are captured using an orthogonal diversity term, thereby reducing the redundant information between views. To enhance the robustness of the model to noise, we explicitly separate the self-representation tensor into a clean tensor and a noise tensor. Additionally, Laplacian manifold regularization is employed to preserve the local structure of high-dimensional data in low-dimensional space. An efficient algorithm is designed to solve the proposed model. Comprehensive experiments are conducted on ten datasets, demonstrating the superior performance of the proposed model.