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A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation
The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symmetric matrix and its perturbation. However, when the matrix dimension is large, computing leading eigenvectors is computationally expensive, limiting the practical use of spectral methods in modern large-scale applications. This paper addresses this problem by proposing an independent Bernoulli sampling scheme and proves that the leading left singular vectors of the subsampled matrix faithfully approximate the target subspace of a low-rank symmet
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-06T02:31:45.000Z
First collected: 2026-09-20T21:12:06.801Z. This is not the publication date.