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Sparse PCA: Convex Relaxations, Algorithms and Applications

In: Handbook on Semidefinite, Conic and Polynomial Optimization

Author

Listed:
  • Youwei Zhang

    (EECS, University of California)

  • Alexandre d’Aspremont

    (ORFE, Princeton University)

  • Laurent El Ghaoui

    (EECS, University of California)

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.

Suggested Citation

  • Youwei Zhang & Alexandre d’Aspremont & Laurent El Ghaoui, 2012. "Sparse PCA: Convex Relaxations, Algorithms and Applications," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 915-940, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-0769-0_31
    DOI: 10.1007/978-1-4614-0769-0_31
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