A Short Note on Improved ROSETA
Numerical Analysis
2017-10-18 v1 Numerical Analysis
Abstract
This note presents a more efficient formulation of the robust online subspace estimation and tracking algorithm (ROSETA) that is capable of identifying and tracking a time-varying low dimensional subspace from incomplete measurements and in the presence of sparse outliers. The algorithm minimizes a robust l1 norm cost function between the observed measurements and their projection onto the estimated subspace. The projection coefficients and sparse outliers are computed using a LASSO solver and the subspace estimate is updated using a proximal point iteration with adaptive parameter selection.
Cite
@article{arxiv.1710.05961,
title = {A Short Note on Improved ROSETA},
author = {Hassan Mansour},
journal= {arXiv preprint arXiv:1710.05961},
year = {2017}
}