# Joint Linked Component Analysis for Multiview Data

arXiv: https://arxiv.org/abs/2406.11761 · HTML: https://mlsanity.com/paper/2406.11761/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Angle-Based Joint and Individual Variation Explained](https://mlsanity.com/paper/1704.02060/) (2017)
- [Structural Learning and Integrative Decomposition of Multi-View Data](https://mlsanity.com/paper/1707.06573/) (2017)
- [MM-PCA: Integrative Analysis of Multi-group and Multi-view Data](https://mlsanity.com/paper/1911.04927/) (2019)

## Abstract (arXiv)

In this work, we propose the joint linked component analysis (jointLCA) for multiview data. Unlike classic methods which extract the shared components in a sequential manner, the objective of jointLCA is to identify the view-specific loading matrices and the rank of the common latent subspace simultaneously. We formulate a matrix decomposition model where a joint structure and an individual structure are present in each data view, which enables us to arrive at a clean svd representation for the cross covariance between any pair of data views. An objective function with a novel penalty term is then proposed to achieve simultaneous estimation and rank selection. In addition, a refitting procedure is employed as a remedy to reduce the shrinkage bias caused by the penalization.