Sparse and integrative principal component analysis for multiview data

We consider dimension reduction of multiview data, which are emerging in scientific studies. Formulating multiview data as multivariate data with block structures corresponding to the different views, or views of data, we estimate top eigenvectors from multiview data that have twofold sparsity, elementwise sparsity and blockwise sparsity. We propose a Fantope-based optimization criterion with multiple penalties to enforce the desired sparsity patterns and a denoising step is employed to handle potential presence of heteroskedastic noise across different data views. An alternating direction method of multipliers (ADMM) algorithm is used for optimization. We derive the 2 pound 2 convergence of the estimated top eigenvectors and establish their sparsity and support recovery properties. Numerical studies are used to illustrate the proposed method. Our code is available in https://github.com/lxiao665/Sparse-and-Integrative-PCA. .