Fast Eigen Decomposition for Low-Rank Matrix Approximation
Numerical Analysis
2017-06-08 v1 Numerical Analysis
Abstract
In this paper we present an efficient algorithm to compute the eigen decomposition of a matrix that is a weighted sum of the self outer products of vectors such as a covariance matrix of data. A well known algorithm to compute the eigen decomposition of such matrices is though the singular value decomposition, which is available only if all the weights are nonnegative. Our proposed algorithm accepts both positive and negative weights.
Cite
@article{arxiv.1706.02069,
title = {Fast Eigen Decomposition for Low-Rank Matrix Approximation},
author = {Youhei Akimoto},
journal= {arXiv preprint arXiv:1706.02069},
year = {2017}
}