@inproceedings{cee216eabc8044319369a3c6643a87be,
title = "An experimental study of balance in matrix factorization",
abstract = "We experimentally examine how gradient descent navigates the landscape of matrix factorization to obtain a global minimum. First, we review the critical points of matrix factorization and introduce a balanced factorization. By focusing on the balanced critical point at the origin and a subspace of unbalanced critical points, we study the effect of balance on gradient descent, including an initially unbalanced factorization and adding a balance-regularizer to the objective in the MF problem. Simulations demonstrate that maintaining a balanced factorization enables faster escape from saddle points and overall faster convergence to a global minimum.",
keywords = "Balance, Gradient descent, Matrix factorization, Non-convex optimization, Saddle points",
author = "Jennifer Hsia and Ramadge, {Peter J.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 55th Annual Conference on Information Sciences and Systems, CISS 2021 ; Conference date: 24-03-2021 Through 26-03-2021",
year = "2021",
month = mar,
day = "24",
doi = "10.1109/CISS50987.2021.9400232",
language = "English (US)",
series = "2021 55th Annual Conference on Information Sciences and Systems, CISS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 55th Annual Conference on Information Sciences and Systems, CISS 2021",
address = "United States",
}