\(L_1\)-quantization and clustering in Banach spaces
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Publication:2437889
DOI10.3103/S1066530710020031zbMath1282.62152MaRDI QIDQ2437889
Publication date: 10 March 2014
Published in: Mathematical Methods of Statistics (Search for Journal in Brave)
Related Items (5)
Convergence of the $k$-Means Minimization Problem using $\Gamma$-Convergence ⋮ Convergence rates for empirical measures of Markov chains in dual and Wasserstein distances ⋮ A fast and recursive algorithm for clustering large datasets with \(k\)-medians ⋮ Posterior asymptotics in Wasserstein metrics on the real line ⋮ Wasserstein upper bounds of the total variation for smooth densities
Uses Software
Cites Work
- Quantitative concentration inequalities for empirical measures on non-compact spaces
- Strong consistency of k-means clustering
- A central limit theorem for k-means clustering
- Transportation cost-information inequalities and applications to random dynamical systems and diffusions.
- Weak convergence and empirical processes. With applications to statistics
- Foundations of quantization for probability distributions
- Functional data analysis.
- On the mathematical foundations of learning
- Convergence of Vector Quantizers with Applications to Optimal Quantization
- On the Performance of Clustering in Hilbert Spaces
- Quantization and the method of<tex>k</tex>-means
- Finding Groups in Data
- Convergent estimators for the l1-median of banach valued random variable
- Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding
- Rates of Convergence of the Functional $k$-Nearest Neighbor Estimate
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