Recently started watching some compression perception, about the content of the J - L lemma, J - L lemma personal understanding about is random projection can reduce the data dimension while maintaining any two data points or sparse signal of Euclidean distance, so I just want to write a program to prove the Euclidean distance between two data points is the same, but only to find out a lot, so want to consult everybody, I proved that method is correct, incorrect or JL lemma should be how to,
The beginner, if you have any advice please write detail, thank you,

 clear all; 
close all; 
clc; 
A=rand (2000, 1); 
B=rand (2000, 1); % % to establish two random matrix 
C=(A - B). ^ 2. 
Short=SQRT (sum (c (:))); Measuring the Euclidean distance between the two matrices % 

Alphabet=[1.732 0-1.732]; Two-thirds prob [=1/6 1/6]; 
V=randsrc (0, 50200 [alphabet; prob]); % build sparse random matrix 
E=v * A; 
F=v * B; 
 % measurementG=(E - F). ^ 2. 
Disdance1=SQRT (sum (g (:))); % measuring compressed the Euclidean distance between the two matrix 

CodePudding user response:

Do bloggers this problem solve?