Stochastic Approximation for Canonical Correlation Analysis
Machine Learning
2018-02-27 v2 Machine Learning
Abstract
We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated gradient (MEG), and achieve -suboptimality in the population objective in iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.
Cite
@article{arxiv.1702.06818,
title = {Stochastic Approximation for Canonical Correlation Analysis},
author = {Raman Arora and Teodor V. Marinov and Poorya Mianjy and Nathan Srebro},
journal= {arXiv preprint arXiv:1702.06818},
year = {2018}
}