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Geometric Optimization in Machine Learning

  • Suvrit Sra
  • Reshad Hosseini
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Machine learning models often rely on sparsity, low-rank, orthogonality, correlation, or graphical structure. The structure of interest in this chapter is geometric, specifically the manifold of positive definite (PD) matrices. Though these matrices recur throughout the applied sciences, our focus is on more recent developments in machine learning and optimization. In particular, we study (i) models that might be nonconvex in the Euclidean sense but are convex along the PD manifold; and (ii) ones that are neither Euclidean nor geodesic convex but are nevertheless amenable to global optimization. We cover basic theory for (i) and (ii); subsequently, we present a scalable Riemannian limited-memory BFGS algorithm (that also applies to other manifolds). We highlight some applications from statistics and machine learning that benefit from the geometric structure studies.

Keywords

Riemannian Manifold Tangent Space Expectation Maximization Descent Direction Positive Definite 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

SS acknowledges partial support from NSF grant IIS-1409802.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Laboratory for Information & Decision Systems (LIDS)Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.School of ECECollege of Engineering, University of TehranTehranIran

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