Sparse canonical correlation analysis
Machine Learning
2017-06-06 v2 Applications
Abstract
Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical correlation analysis breaks down. We propose a sparse canonical correlation analysis by adding l1 constraints on the canonical vectors and show how to solve it efficiently using linearized alternating direction method of multipliers (ADMM) and using TFOCS as a black box. We illustrate this idea on simulated data.
Keywords
Cite
@article{arxiv.1705.10865,
title = {Sparse canonical correlation analysis},
author = {Xiaotong Suo and Victor Minden and Bradley Nelson and Robert Tibshirani and Michael Saunders},
journal= {arXiv preprint arXiv:1705.10865},
year = {2017}
}