Representation and coding of signal geometry
From MaRDI portal
Publication:4603712
DOI10.1093/imaiai/iax002zbMath1386.94021arXiv1512.07636OpenAlexW2963591649MaRDI QIDQ4603712
Hassan Mansour, Shantanu Rane, Petros T. Boufounos
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/1512.07636
quantizationkernel methodsdimensionality reductionrandomized embeddingscoding for inferencedistance representations
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Toward a unified theory of sparse dimensionality reduction in Euclidean space
- Dimensionality reduction with subgaussian matrices: a unified theory
- Democracy in action: quantization, saturation, and compressive sensing
- The restricted isometry property and its implications for compressed sensing
- Random projections of smooth manifolds
- A simple proof of the restricted isometry property for random matrices
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Problems and results in extremal combinatorics. I.
- The geometry of graphs and some of its algorithmic applications
- Dimension reduction by random hyperplane tessellations
- New analysis of manifold embeddings and signal recovery from compressive measurements
- One-Bit Compressed Sensing by Linear Programming
- 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
- Extensions of Lipschitz mappings into a Hilbert space
- Stable distributions, pseudorandom generators, embeddings, and data stream computation
- On the impossibility of dimension reduction in l 1
- Coding for computing
- NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings
- Sparse recovery from saturated measurements
- An elementary proof of a theorem of Johnson and Lindenstrauss
- Robust Recovery of Signals From a Structured Union of Subspaces
- Sampling Theorems for Signals From the Union of Finite-Dimensional Linear Subspaces
- Universal Rate-Efficient Scalar Quantization
- Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions
- Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine
- Functional Compression Through Graph Coloring
- Model-Based Compressive Sensing
- Stabilizing Nonuniformly Quantized Compressed Sensing With Scalar Companders
- Locality-sensitive hashing scheme based on p-stable distributions
- Stable signal recovery from incomplete and inaccurate measurements
- Low distortion embeddings for edit distance
- Compressed sensing
This page was built for publication: Representation and coding of signal geometry