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Convex and discrete geometry Definition

Concentration of measure

Builds on Metric spaceProbability measureGaussian measure

Definition

Let (X,d)(X, d) be a metric space and μ\mu a Borel probability measure on XX. For a Borel set A⊆XA \subseteq X and r>0r > 0, let Ar={x∈X:d(x,A)<r}A_r = \set{x \in X : d(x, A) < r}. The concentration function of (X,d,μ)(X, d, \mu) is α(r)=sup⁡{1−μ(Ar):A⊆X Borel, μ(A)≥1/2},r>0.\alpha(r) = \sup \set{1 - \mu(A_r) : A \subseteq X \text{ Borel}, \ \mu(A) \ge 1/2}, \qquad r > 0. A sequence (Xk,dk,μk)(X_k, d_k, \mu_k) of such spaces exhibits concentration of measure, and is called a normal Lévy family, if there are constants C,c>0C, c > 0 with αk(r)≤Ce−ckr2for all k≥1 and r>0.\alpha_k(r) \le C e^{-c k r^2} \quad \text{for all } k \ge 1 \text{ and } r > 0.

Remarks

Example: take the unit sphere SkS^{k} of ℝk+1\R^{k+1} with the geodesic metric and the normalized surface measure. Lévy's isoperimetric inequality gives such a bound with absolute constants CC and cc, so the spheres form a normal Lévy family. The Gaussian measure γn\gamma_n on ℝn\R^n concentrates with a bound that does not depend on the dimension: if γn(A)≥1/2\gamma_n(A) \ge 1/2, then γn(Ar)≥1−12e−r2/2\gamma_n(A_r) \ge 1 - \frac{1}{2} e^{-r^2/2}. Non-example: the two-point space {0,1}\set{0, 1} with d(0,1)=1d(0, 1) = 1 and μ({0})=μ({1})=1/2\mu(\set{0}) = \mu(\set{1}) = 1/2; for A={0}A = \set{0} and r≤1r \le 1 one has Ar=AA_r = A, so α(r)≥1/2\alpha(r) \ge 1/2 for r≤1r \le 1. An equivalent form: μ({x:∣f(x)−Mf∣≥r})≤2α(r)\mu(\set{x : \abs{f(x) - M_f} \ge r}) \le 2 \alpha(r) for every 11-Lipschitz function f ⁣:X→ℝf \colon X \to \R with median MfM_f.

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Further reading

  1. Web https://en.wikipedia.org/wiki/Concentration_of_measure
About this page
Tag17BH73YHVT
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Kinddefinition
WritingW0 stub
MSC52A23, 60E15, 60B11
DefinesConcentration of measure
UsesMetric space, Probability measure, Gaussian measure

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