A Short Note on Improved ROSETA
Incremental improvement to an existing algorithm for robust subspace tracking, relevant to signal processing and computer vision applications.
This work improves the ROSETA algorithm for robust online subspace tracking from incomplete and corrupted data, achieving faster convergence and better tracking accuracy with adaptive parameter selection.
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.