Time for dithering: fast and quantized random embeddings via the restricted isometry property
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Publication:4603715
DOI10.1093/imaiai/iax004zbMath1386.94031arXiv1607.00816OpenAlexW2964285922MaRDI QIDQ4603715
Laurent Jacques, Valerio Cambareri
Publication date: 19 February 2018
Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1607.00816
quantizationdimensionality reductioncompressive sensingrestricted isometry propertyrandom projectionsditherfast and structured sensing matriceslow-complexity signal modelsnonlnear embeddingsquantized rank-one projections
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Cites Work
- Unnamed Item
- A unified framework for linear dimensionality reduction in L1
- Low rank matrix recovery from rank one measurements
- A mathematical introduction to compressive sensing
- Restricted isometries for partial random circulant matrices
- Compressed sensing with coherent and redundant dictionaries
- Uniform recovery of fusion frame structured sparse signals
- Two observations regarding embedding subsets of Euclidean spaces in normed spaces
- A simple proof of the restricted isometry property for random matrices
- Uniform uncertainty principle for Bernoulli and subgaussian ensembles
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- The convex geometry of linear inverse problems
- Dimension reduction by random hyperplane tessellations
- ROP: matrix recovery via rank-one projections
- Compressed sensing and its applications. MATHEON workshop, Berlin, Germany, December 2013
- New analysis of manifold embeddings and signal recovery from compressive measurements
- Empirical processes and random projections
- Error Decay of (almost) Consistent Signal Estimations from Quantized Gaussian Random Projections
- A Quantized Johnson–Lindenstrauss Lemma: The Finding of Buffon’s Needle
- Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
- Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach
- Self-calibration and biconvex compressive sensing
- On sparse reconstruction from Fourier and Gaussian measurements
- Decoding by Linear Programming
- Compressed Sensing and Redundant Dictionaries
- Compressive Sensing by Random Convolution
- Conference on Modern Analysis and Probability
- Quantization
- An elementary proof of a theorem of Johnson and Lindenstrauss
- Isometric sketching of any set via the Restricted Isometry Property
- Universal Rate-Efficient Scalar Quantization
- Sampling and Reconstructing Signals From a Union of Linear Subspaces
- Recipes for Stable Linear Embeddings From Hilbert Spaces to $ {\mathbb {R}}^{m}$
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Recovering Low-Rank Matrices From Few Coefficients in Any Basis
- Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine
- Stabilizing Nonuniformly Quantized Compressed Sensing With Scalar Companders
- Locality-sensitive hashing scheme based on p-stable distributions
- An Introduction to Matrix Concentration Inequalities
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