Core-Sets: Updated Survey
From MaRDI portal
Publication:3297370
DOI10.1007/978-3-030-29349-9_2zbMath1436.62707OpenAlexW2981895326MaRDI QIDQ3297370
Publication date: 3 July 2020
Published in: Sampling Techniques for Supervised or Unsupervised Tasks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-29349-9_2
Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05) Packing and covering in (n) dimensions (aspects of discrete geometry) (52C17) Combinatorial aspects of packing and covering (05B40) Statistical aspects of big data and data science (62R07)
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Cites Work
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- \(\epsilon\)-nets and simplex range queries
- A constant-factor approximation algorithm for the \(k\)-median problem
- Approximations and optimal geometric divide-and-conquer
- Optimal core-sets for balls
- Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
- Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
- Randomized Dimensionality Reduction for <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Means Clustering
- Uniform Sampling for Matrix Approximation
- Approximating extent measures of points
- Randomized Algorithms for Matrices and Data
- Learnability and the Vapnik-Chervonenkis dimension
- The EM Algorithm and Extensions, 2E
- Data Streams: Algorithms and Applications
- On Coresets for k-Median and k-Means Clustering in Metric and Euclidean Spaces and Their Applications
- On coresets for k-means and k-median clustering
- Matrix approximation and projective clustering via volume sampling
- Sampling algorithms for l2 regression and applications
- Bounded-Hop Energy-Efficient Broadcast in Low-Dimensional Metrics Via Coresets
- A PTAS for k-means clustering based on weak coresets
- Bi-criteria linear-time approximations for generalized k-mean/median/center
- The Planar k-Means Problem is NP-Hard
- Adaptive Sampling for k-Means Clustering
- Shape Fitting on Point Sets with Probability Distributions
- Decomposable searching problems I. Static-to-dynamic transformation
- Projective clustering in high dimensions using core-sets
- Approximation algorithms for projective clustering
- Neural Network Learning
- Twice-Ramanujan Sparsifiers
- Numerical linear algebra in the streaming model
- Private coresets
- Improved bounds on weak ε-nets for convex sets
- Improved Combinatorial Algorithms for Facility Location Problems
- Probability Inequalities for Sums of Bounded Random Variables
- New constructions of weak epsilon-nets
- Smaller coresets for k-median and k-means clustering
- Coresets for Discrete Integration and Clustering
- The effectiveness of lloyd-type methods for the k-means problem
- Comparing distributions and shapes using the kernel distance
- A unified framework for approximating and clustering data
- FSTTCS 2004: Foundations of Software Technology and Theoretical Computer Science
- Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
- FSTTCS 2005: Foundations of Software Technology and Theoretical Computer Science
- Improved bounds on the sample complexity of learning