Approximation of points on low-dimensional manifolds via random linear projections

DOI10.1093/imaiai/iat001zbMath1354.94013arXiv1204.3337OpenAlexW2963030988MaRDI QIDQ4982420

Mark A. Iwen, Mauro Maggioni

Publication date: 9 April 2015

Published in: Information and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1204.3337

zbMATH Keywords

manifoldssignal recoverydimensionality reductioncompressed sensingrandom projectionsJohnson-Lindenstrauss lemma

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data ⋮ Multiscale geometric methods for data sets. I: Multiscale SVD, noise and curvature. ⋮ On fast Johnson-Lindenstrauss embeddings of compact submanifolds of \(\mathbb{R}^N\) with boundary ⋮ What happens to a manifold under a bi-Lipschitz map? ⋮ Testing the manifold hypothesis ⋮ On recovery guarantees for one-bit compressed sensing on manifolds ⋮ Unnamed Item ⋮ New analysis of manifold embeddings and signal recovery from compressive measurements

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