Sparse PCA: Convex Relaxations, Algorithms and Applications

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Authors: Youwei Zhang, Alexandre d'Aspremont, Laurent El Ghaoui.

Status: To appear as a chapter in the forthcoming Handbook of Semidefinite, Cone and Polynomial Optimization: Theory, Algorithms, Software and Applications, Miguel Anjos and Jean Bernard Lasserre, Editors, Kluwer.

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.

Bibtex reference:

@incollection {ZAE:11,
   author =    "Zhang, Y. and {d'Aspremont}, A. and {El Ghaoui}, L.",
   title =	"Sparse {PCA}: Convex Relaxations, Algorithms and Applications",
   booktitle = "Handbook on Semidenite, Cone and Polynomial Optimization:
                      Theory, Algorithms, Software and Applications",
   publisher = "Springer",
   editor =    "Anjos, M. and Lasserre, J.B.",
   year = 	2011,
   note = "To appear"
}