Lecture 5 — September 8 , 2016
نویسندگان
چکیده
1 Overview In the last lecture we took a more in depth look at Chernoff Bounds and introduced subgaussian and subexponential variables. In this lecture we will continue talking about subgaussian variables and related random variables – subexponential and subgamma, and finally we will give a proof of famous Johnson-Lindenstrauss lemma using property of subgaussian/subgamma variables. Definition 1. A random variable X is subgaussian with µ = E(X) and parameter σ if it satisfies any of the following 3 properties:
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