Robust Sparse Subspace Tracking from Corrupted Data Observations

Ta Giang Thuy Loan; Hoang-Lan Nguyen; Nguyen Thi Ngoc Lan; Do Hai Son; Tran Thi Thuy Quynh; Nguyen Linh Trung; Karim Abed-Meraim; Thanh Trung Le

arXiv · 2509.16585

Robust Sparse Subspace Tracking from Corrupted Data Observations

Ta Giang Thuy Loan·Hoang-Lan Nguyen·Nguyen Thi Ngoc Lan·Do Hai Son·Tran Thi Thuy Quynh·Nguyen Linh Trung·Karim Abed-Meraim·Thanh Trung Le

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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.

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Ta Giang Thuy Loan, Hoang-Lan Nguyen, Nguyen Thi Ngoc Lan, Do Hai Son, Tran Thi Thuy Quynh, Nguyen Linh Trung, Karim Abed-Meraim, Thanh Trung Le. 2025-09-20. Robust Sparse Subspace Tracking from Corrupted Data Observations. https://arxiv.org/abs/2509.16585

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