English

Joint universal lossy coding and identification of i.i.d. vector sources

Information Theory 2016-11-15 v2 Machine Learning math.IT

Abstract

The problem of joint universal source coding and modeling, addressed by Rissanen in the context of lossless codes, is generalized to fixed-rate lossy coding of continuous-alphabet memoryless sources. We show that, for bounded distortion measures, any compactly parametrized family of i.i.d. real vector sources with absolutely continuous marginals (satisfying appropriate smoothness and Vapnik--Chervonenkis learnability conditions) admits a joint scheme for universal lossy block coding and parameter estimation, and give nonasymptotic estimates of convergence rates for distortion redundancies and variational distances between the active source and the estimated source. We also present explicit examples of parametric sources admitting such joint universal compression and modeling schemes.

Keywords

Cite

@article{arxiv.cs/0601074,
  title  = {Joint universal lossy coding and identification of i.i.d. vector sources},
  author = {Maxim Raginsky},
  journal= {arXiv preprint arXiv:cs/0601074},
  year   = {2016}
}

Comments

5 (or 6 with hyperref) pages, 2 eps figures; final version to appear in Proc. ISIT 2006; full version of this paper was submitted to IEEE Trans. Inform. Theory and can be found at cs.IT/0512015

R2 v1 2026-07-22T12:24:57.618Z