# Sparse Weighted Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1710.04792 · HTML: https://mlsanity.com/paper/1710.04792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection](https://mlsanity.com/paper/2308.06740/) (2023)
- [Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations](https://mlsanity.com/paper/2504.11610/) (2025)

## Abstract (arXiv)

Given two data matrices X and Y, sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Yv. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel sparse weighted canonical correlation analysis (SWCCA), where weights are used for regularizing different samples. We solve the L0-regularized SWCCA (L0-SWCCA) using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. Lastly, we consider also SWCCA with different penalties like LASSO (Least absolute shrinkage and selection operator) and Group LASSO, and extend it for integrating more than three data matrices.