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Journal Articles Journal of Machine Learning Research Year : 2022

Convergence rate of optimal quantization grids and application to empirical measure

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Yating Liu
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Abstract

We study the convergence rate of the optimal quantization for a probability measure sequence $(\mu_{n})_{n\in\mathbb{N}^{*}}$ on $\mathbb{R}^{d}$ converging in the Wasserstein distance in two aspects: the first one is the convergence rate of optimal quantizer $x^{(n)}\in(\mathbb{R}^{d})^{K}$ of $\mu_{n}$ at level $K$; the other one is the convergence rate of the distortion function valued at $x^{(n)}$, called the "performance" of $x^{(n)}$. Moreover, we also study the mean performance of the optimal quantization for the empirical measure of a distribution $\mu$ with finite second moment but possibly unbounded support. As an application, we show that the mean performance for the empirical measure of the multidimensional normal distribution $\mathcal{N}(m, \Sigma)$ and of distributions with hyper-exponential tails behave like $\mathcal{O}(\frac{\log n}{\sqrt{n}})$. This extends the results from [BDL08] obtained for compactly supported distribution. We also derive an upper bound which is sharper in the quantization level $K$ but suboptimal in $n$ by applying results in [FG15].

Dates and versions

hal-03890795 , version 1 (08-12-2022)

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Cite

Yating Liu, Gilles Pagès. Convergence rate of optimal quantization grids and application to empirical measure. Journal of Machine Learning Research, 2022, 21 (1), pp.3352-3387. ⟨hal-03890795⟩
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