Sparse PCA: Convex Relaxations, Algorithms and Applications
Abstract
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. Unfortunately, this problem is also combinatorially hard and we discuss convex relaxation techniques that efficiently produce good approximate solutions. We then describe several algorithms solving these relaxations as well as greedy algorithms that iteratively improve the solution quality. Finally, we illustrate sparse PCA in several applications, ranging from senate voting and finance to news data.
Cite
@article{arxiv.1011.3781,
title = {Sparse PCA: Convex Relaxations, Algorithms and Applications},
author = {Youwei Zhang and Alexandre d'Aspremont and Laurent El Ghaoui},
journal= {arXiv preprint arXiv:1011.3781},
year = {2010}
}
Comments
To appear in "Handbook on Semidefinite, Cone and Polynomial Optimization", M. Anjos and J.B. Lasserre, editors. This revision includes ROC curves for greedy algorithms