Efficient Multilevel Eigensolvers with Applications to Data Analysis Tasks

Authors:
Dan Kushnir;Meirav Galun;Achi Brandt
Affiliations:
Yale University, New Haven;Weizmann Institute of Science, Rehovot;Weizmann Institute of Science, Rehovot
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2010

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Abstract

Multigrid solvers proved very efficient for solving massive systems of equations in various fields. These solvers are based on iterative relaxation schemes together with the approximation of the “smooth” error function on a coarser level (grid). We present two efficient multilevel eigensolvers for solving massive eigenvalue problems that emerge in data analysis tasks. The first solver, a version of classical algebraic multigrid (AMG), is applied to eigenproblems arising in clustering, image segmentation, and dimensionality reduction, demonstrating an order of magnitude speedup compared to the popular Lanczos algorithm. The second solver is based on a new, much more accurate interpolation scheme. It enables calculating a large number of eigenvectors very inexpensively.