1. Dimensionality Reduction

2. Random Projections Johnson-Lindenstrauss lemma For:
any (sufficiently large) set S of M points in Rn
k = O(e-2lnM)
There exists a linear map f:S Rk, such that
(1- e) ||u-v||2 ≤ ||f(u)-f(v)||2 ≤ (1+ e)||u-v||2 for u,v in S
Random projection is good with constant probability

3. Random Projection Set k = O(e-2lnM)
Select k random n-dimensional vectors
(an approach is to select k gaussian distributed vectors with variance 0 and mean value 1: N(1,0) )
Project the original points into the k vectors.
The resulting k-dimensional space approximately preserves the distances with high probability

4. “Database friendly” RP
Achlioptas showed that it is possible to do random projections with the same guarantees using only {1, -1} or {1, 0, -1}
Thus you need to do only additions and subtractions, not multiplications

5. Theorem Let P a set of n points in Rd, stored as a n x d matrix A.
Given e, b >0, let
For integer k > k0 let R be a d x k matrix with R(i, j) = {rij}, with elements that are generated randomly and independently from the following distribution:
+1 with probability 1/2
rij =
-1 with probability 1/2

6. Let and let f: Rd  Rk
With probability at least 1-n-b, for all u, v in P
(1- e) ||u-v||2 ≤ ||f(u)-f(v)||2 ≤ (1+ e)||u-v||2

7. The same is true if you use:
+1 with probability 1/6
rij = with probability 2/3
-1 with probability 1/6

8. The proof is similar to previous in spirit, but differs in details
The proof is similar to previous in spirit, but differs in details. Again, we need to show that the length of a vector concentrates around its mean value in the projected space.
We show that the worst case vectors for this projection matrix are the vectors:
and that the even moments of the projection of these vectors are dominated by the corresponding moments of the spherically symmetric projections.