Robust Sparse Subspace Tracking from Corrupted Data Observations
This work addresses the problem of robust dynamic subspace estimation and tracking in high-dimensional data streams corrupted by non-Gaussian noise and sparse outliers. To tackle this, we propose a novel online algorithm grounded in α-divergence minimization. By incorporating α-divergence into the subspace optimization objective, the method explicitly enhances robustness against heavy-tailed noise and impulsive data corruptions. Further, it integrates sparse subspace modeling with a low-rank iterative update scheme, ensuring theoretical convergence while substantially reducing computational and memory complexity. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed approach achieves significantly lower subspace tracking error and higher direction-of-arrival (DOA) estimation accuracy compared to state-of-the-art robust PCA and adaptive subspace methods. These results validate its effectiveness and practicality for dynamic signal processing under challenging non-Gaussian and sparse corruption conditions.