\( \varepsilon \)-isometric dimension reduction for incompressible subsets of \(\ell_p\)
DOI10.1007/s00454-023-00587-warXiv2109.06602MaRDI QIDQ6145674
Publication date: 9 January 2024
Published in: Discrete \& Computational Geometry (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.06602
Spaces of measurable functions ((L^p)-spaces, Orlicz spaces, Köthe function spaces, Lorentz spaces, rearrangement invariant spaces, ideal spaces, etc.) (46E30) Probabilistic methods in Banach space theory (46B09) Embeddings of discrete metric spaces into Banach spaces; applications in topology and computer science (46B85) Metric embeddings as related to computational problems and algorithms (68R12)
Cites Work
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- Toward a unified theory of sparse dimensionality reduction in Euclidean space
- Dimensionality reduction with subgaussian matrices: a unified theory
- Isometric embedding in \(\ell_ p\)-spaces
- Two observations regarding embedding subsets of Euclidean spaces in normed spaces
- The Johnson-Lindenstrauss lemma and the sphericity of some graphs
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Approximate nearest neighbor search for \(\ell_{p}\)-spaces \((2 < p < \infty)\) via embeddings
- Embedding the diamond graph in \(L_p\) and dimension reduction in \(L_1\)
- On the distortion required for embedding finite metric spaces into normed spaces
- Metric embeddings. Bilipschitz and coarse embeddings into Banach spaces
- Approximate Carathéodory's theorem in uniformly smooth Banach spaces
- Sampling discretization and related problems
- Dimension reduction by random hyperplane tessellations
- Impossibility of dimension reduction in the nuclear norm
- The Johnson-Lindenstrauss lemma almost characterizes Hilbert space, but not quite
- An algorithmic theory of learning: Robust concepts and random projection
- Empirical processes and random projections
- Metric structures in \(L_1\): dimension, snowflakes, and average distortion
- Revisiting the approximate Carathéodory problem via the Frank-Wolfe algorithm
- An Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform
- A Quantized Johnson–Lindenstrauss Lemma: The Finding of Buffon’s Needle
- New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property
- Sparser Johnson-Lindenstrauss Transforms
- METRIC DIMENSION REDUCTION: A SNAPSHOT OF THE RIBE PROGRAM
- Extensions of Lipschitz mappings into a Hilbert space
- On the Volume of the Intersection of Two L n p Balls
- On variants of the Johnson–Lindenstrauss lemma
- On the impossibility of dimension reduction in l 1
- Nearest-neighbor-preserving embeddings
- Concentration of the distance in finite dimensional normed spaces
- Approximating Nash Equilibria and Dense Subgraphs via an Approximate Version of Carathéodory's Theorem
- Small Width, Low Distortions: Quantized Random Embeddings of Low-complexity Sets
- High-Dimensional Probability
- An elementary proof of a theorem of Johnson and Lindenstrauss
- Bounds on Dimension Reduction in the Nuclear Norm
- The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors
- A Simple Tool for Bounding the Deviation of Random Matrices on Geometric Sets
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