Spectral matching of bipartite graphs
Design and application of hybrid intelligent systems
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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This paper addresses the problem of approximating an eigenvector belonging to the largest eigenvalue of a symmetric positive definite matrix by the power method. We assume that the starting vector is randomly chosen with uniform distribution over the unit sphere.This paper provides lower and upper as well as asymptotic bounds on the randomized error in the ${\cal L}_p$ sense, $p\in[1,+\infty]$. We prove that it is impossible to achieve sharp bounds that are independent of the ratio between the two largest eigenvalues. This should be contrasted to the problem of approximating the largest eigenvalue, for which Kuczyński and Woźniakowski [ SIAM J. Matrix Anal. Appl., 13 (1992), pp. 1094--1122] proved that it is possible to bound the randomized error at the kth step with a quantity that depends only on k and on the size of the matrix.We prove that the rate of convergence depends on the ratio of the two largest eigenvalues, on their multiplicities, and on the particular norm. The rate of convergence is at most linear in the ratio of the two largest eigenvalues.