Robust Sparse Subspace Tracking from Corrupted Data Observations
By: Ta Giang Thuy Loan , Hoang-Lan Nguyen , Nguyen Thi Ngoc Lan and more
Potential Business Impact:
Finds hidden patterns even with messy data.
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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