get_joint_decomposition_robustH

Matrix. The basis of the joint space (dimension n x joint_rank).

joint_scores

Boolean. Do we compute the full J, I matrices or just the SVD (set to FALSE to save memory).

full

Computes the individual matrix for a data block

A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

get_joint_decomposition_robustH: Computes the individual matrix for a data block

Description

Usage

Arguments