A Short Note on Improved ROSETA

TL;DR

This paper introduces an improved, more efficient version of the ROSETA algorithm for robust online subspace estimation and tracking, capable of handling incomplete data and sparse outliers with enhanced computational performance.

Contribution

It presents a novel formulation of ROSETA that reduces computational complexity while maintaining robustness in tracking time-varying subspaces from incomplete measurements.

Findings

Enhanced efficiency over previous ROSETA implementations

Effective handling of sparse outliers in real-time tracking

Robust subspace estimation with incomplete data

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.