@inproceedings{888374a5ab114010b677103d90c494e2,
title = "Low-rank approximations with applications to principal singular component learning systems",
abstract = "In this paper, we present several dynamical systems for efficient and accurate computation of optimal low rank approximation of a real matrix. The proposed dynamical systems are gradient flows or weighted gradient flows derived from unconstrained optimization of certain objective functions. These systems are then modified to obtain power-like methods for computing a few dominant singular triplets of very large matrices simultaneously rather than just one at a time, by incorporating upper-triangular and diagonal matrices. The validity of the proposed algorithms was demonstrated through numerical experiments.",
keywords = "Asymptotic stability, Constrained optimization, Dynamical system, Global convergence, Principal singular flow, SVD, Stiefel manifold",
author = "Hasan, {Mohammed A.}",
year = "2008",
doi = "10.1109/CDC.2008.4739112",
language = "English (US)",
isbn = "9781424431243",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3293--3298",
booktitle = "Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008",
note = "47th IEEE Conference on Decision and Control, CDC 2008 ; Conference date: 09-12-2008 Through 11-12-2008",
}