Invariance principle of random projection for the norm

12/01/2021
by   Juntao Duan, et al.
0

Johnson-Lindenstrauss guarantees certain topological structure is preserved under random projections when embedding high dimensional deterministic vectors to low dimensional vectors. In this work, we try to understand how random projections affect norms of random vectors. In particular we prove the distribution of norm of random vectors X ∈ℝ^n, whose entries are i.i.d. random variables, is preserved by random projection S:ℝ^n →ℝ^m. More precisely, X^TS^TSX - mn/√(σ^2 m^2n+2mn^2)𝒩(0,1)

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/30/2018

Rigorous Restricted Isometry Property of Low-Dimensional Subspaces

Dimensionality reduction is in demand to reduce the complexity of solvin...
research
06/18/2012

Is margin preserved after random projection?

Random projections have been applied in many machine learning algorithms...
research
11/16/2015

Binary embeddings with structured hashed projections

We consider the hashing mechanism for constructing binary embeddings, th...
research
02/07/2023

OPORP: One Permutation + One Random Projection

Consider two D-dimensional data vectors (e.g., embeddings): u, v. In man...
research
06/29/2020

Binary Random Projections with Controllable Sparsity Patterns

Random projection is often used to project higher-dimensional vectors on...
research
04/20/2016

Random Projection Estimation of Discrete-Choice Models with Large Choice Sets

We introduce sparse random projection, an important dimension-reduction ...
research
12/08/2022

Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection

Bias elimination and recent probing studies attempt to remove specific i...

Please sign up or login with your details

Forgot password? Click here to reset