The minimax distortion redundancy in empirical quantizer design
From MaRDI portal
Publication:4701158
DOI10.1109/18.705560zbMath0964.94015OpenAlexW2137216203MaRDI QIDQ4701158
Bartlett, Peter L., Gábor Lugosi, Tamás Linder
Publication date: 21 November 1999
Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10230/743
data compressionboundsdesign algorithmvector quantizersexpected distortion redundancymean-squared distortion
Related Items
Convergence of the $k$-Means Minimization Problem using $\Gamma$-Convergence, A framework for statistical clustering with constant time approximation algorithms for \(K\)-median and \(K\)-means clustering, Fast rates for empirical vector quantization, Statistical learning guarantees for compressive clustering and compressive mixture modeling, A statistical view of clustering performance through the theory of \(U\)-processes, Empirical risk minimization for heavy-tailed losses, On Hölder fields clustering, Consistency of spectral clustering, A \(k\)-points-based distance for robust geometric inference, A notion of stability for \(k\)-means clustering, Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes, Vector quantization and clustering in the presence of censoring, A quasi-Bayesian perspective to online clustering, Robust \(k\)-means clustering for distributions with two moments, On strong consistency of kernel \(k\)-means: a Rademacher complexity approach, Nonasymptotic bounds for vector quantization in Hilbert spaces, Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps
