Robust Sparse Subspace Tracking from Corrupted Data Observations
Signal Processing
2025-09-23 v1 Information Theory
math.IT
Abstract
Subspace tracking is a fundamental problem in signal processing, where the goal is to estimate and track the underlying subspace that spans a sequence of data streams over time. In high-dimensional settings, data samples are often corrupted by non-Gaussian noises and may exhibit sparsity. This paper explores the alpha divergence for sparse subspace estimation and tracking, offering robustness to data corruption. The proposed method outperforms the state-of-the-art robust subspace tracking methods while achieving a low computational complexity and memory storage. Several experiments are conducted to demonstrate its effectiveness in robust subspace tracking and direction-of-arrival (DOA) estimation.
Cite
@article{arxiv.2509.16585,
title = {Robust Sparse Subspace Tracking from Corrupted Data Observations},
author = {Ta Giang Thuy Loan and Hoang-Lan Nguyen and Nguyen Thi Ngoc Lan and Do Hai Son and Tran Thi Thuy Quynh and Nguyen Linh Trung and Karim Abed-Meraim and Thanh Trung Le},
journal= {arXiv preprint arXiv:2509.16585},
year = {2025}
}