svd_reconstruction

<p>List. List with entries 'u', 'd', and 'v'from the svd function.</p>

decomposition

<p>Computes UDV^T to get the approximate (or full) X matrix.</p>

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>.

Erica Ponzi

svd_reconstruction: Reconstruces the original matrix from its robust SVD.

Description

Usage

Arguments

Value