A Perturbation Bound on the Subspace Estimator from Canonical Projections
arXiv:2206.14278v1h-index: 11Has Code
Originality Incremental advance
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This work provides a theoretical foundation for subspace estimation in noisy settings, which is incremental but important for applications like matrix completion and subspace clustering.
The paper derived a perturbation bound for the optimal subspace estimator when canonical projections are contaminated by noise, addressing a fundamental problem in matrix completion and subspace clustering.
This paper derives a perturbation bound on the optimal subspace estimator obtained from a subset of its canonical projections contaminated by noise. This fundamental result has important implications in matrix completion, subspace clustering, and related problems.