Universal Ls-rate-optimality of Lr-optimal quantizers by dilatation and contraction

Sagna, Abass. "Universal Ls-rate-optimality of Lr-optimal quantizers by dilatation and contraction." ESAIM: Probability and Statistics 13 (2009): 218-246. <http://eudml.org/doc/250635>.

@article{Sagna2009,
abstract = { We investigate in this paper the properties of some dilatations or contractions of a sequence (αn)n≥1 of Lr-optimal quantizers of an $\mathbb\{R\}^d$-valued random vector $X \in L^r(\mathbb\{P\})$ defined in the probability space $(\Omega,\mathcal\{A\},\mathbb\{P\})$ with distribution $\mathbb\{P\}_\{X\} = P$. To be precise, we investigate the Ls-quantization rate of sequences $\alpha_n^\{\theta,\mu\} = \mu + \theta(\alpha_n-\mu)=\\{\mu + \theta(a-\mu), \ a \in \alpha_n \\}$ when $\theta \in \mathbb\{R\}_\{+\}^\{\star\}, \mu \in \mathbb\{R\}, s \in (0,r)$ or s ∈ (r, +∞) and $X \in L^s(\mathbb\{P\})$. We show that for a wide family of distributions, one may always find parameters (θ,µ) such that (αnθ,µ)n≥1 is Ls-rate-optimal. For the Gaussian and the exponential distributions we show the existence of a couple (θ*,µ*) such that (αθ*,µ*)n≥1 also satisfies the so-called Ls-empirical measure theorem. Our conjecture, confirmed by numerical experiments, is that such sequences are asymptotically Ls-optimal. In both cases the sequence (αθ*,µ*)n≥1 is incredibly close to Ls-optimality. However we show (see Rem. 5.4) that this last sequence is not Ls-optimal (e.g. when s = 2, r = 1) for the exponential distribution. },
author = {Sagna, Abass},
journal = {ESAIM: Probability and Statistics},
keywords = {Rate-optimal quantizers; empirical measure theorem; dilatation; Lloyd algorithm; rate-optimal quantizers; Gaussian distribution; Lloyd's algorithm},
language = {eng},
month = {6},
pages = {218-246},
publisher = {EDP Sciences},
title = {Universal Ls-rate-optimality of Lr-optimal quantizers by dilatation and contraction},
url = {http://eudml.org/doc/250635},
volume = {13},
year = {2009},
}

TY - JOUR
AU - Sagna, Abass
TI - Universal Ls-rate-optimality of Lr-optimal quantizers by dilatation and contraction
JO - ESAIM: Probability and Statistics
DA - 2009/6//
PB - EDP Sciences
VL - 13
SP - 218
EP - 246
AB - We investigate in this paper the properties of some dilatations or contractions of a sequence (αn)n≥1 of Lr-optimal quantizers of an $\mathbb{R}^d$-valued random vector $X \in L^r(\mathbb{P})$ defined in the probability space $(\Omega,\mathcal{A},\mathbb{P})$ with distribution $\mathbb{P}_{X} = P$. To be precise, we investigate the Ls-quantization rate of sequences $\alpha_n^{\theta,\mu} = \mu + \theta(\alpha_n-\mu)=\{\mu + \theta(a-\mu), \ a \in \alpha_n \}$ when $\theta \in \mathbb{R}_{+}^{\star}, \mu \in \mathbb{R}, s \in (0,r)$ or s ∈ (r, +∞) and $X \in L^s(\mathbb{P})$. We show that for a wide family of distributions, one may always find parameters (θ,µ) such that (αnθ,µ)n≥1 is Ls-rate-optimal. For the Gaussian and the exponential distributions we show the existence of a couple (θ*,µ*) such that (αθ*,µ*)n≥1 also satisfies the so-called Ls-empirical measure theorem. Our conjecture, confirmed by numerical experiments, is that such sequences are asymptotically Ls-optimal. In both cases the sequence (αθ*,µ*)n≥1 is incredibly close to Ls-optimality. However we show (see Rem. 5.4) that this last sequence is not Ls-optimal (e.g. when s = 2, r = 1) for the exponential distribution.
LA - eng
KW - Rate-optimal quantizers; empirical measure theorem; dilatation; Lloyd algorithm; rate-optimal quantizers; Gaussian distribution; Lloyd's algorithm
UR - http://eudml.org/doc/250635
ER -