D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets
D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets
A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing …