Optimal terminal dimensionality reduction in Euclidean space

10/22/2018
by   Shyam Narayanan, et al.
0

Let ε∈(0,1) and X⊂ R^d be arbitrary with |X| having size n>1. The Johnson-Lindenstrauss lemma states there exists f:X→ R^m with m = O(ε^-2 n) such that ∀ x∈ X ∀ y∈ X, x-y_2 <f(x)-f(y)_2 < (1+ε)x-y_2 . We show that a strictly stronger version of this statement holds, answering one of the main open questions of [MMMR18]: "∀ y∈ X" in the above statement may be replaced with "∀ y∈ R^d", so that f not only preserves distances within X, but also distances to X from the rest of space. Previously this stronger version was only known with the worse bound m = O(ε^-4 n). Our proof is via a tighter analysis of (a specific instantiation of) the embedding recipe of [MMMR18].

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/12/2021

Dimensionality Reduction for k-Distance Applied to Persistent Homology

Given a set P of n points and a constant k, we are interested in computi...
research
02/13/2023

Sparse Dimensionality Reduction Revisited

The sparse Johnson-Lindenstrauss transform is one of the central techniq...
research
11/17/2021

On prescribing total preorders and linear orders to pairwise distances of points in Euclidean space

We show that any total preorder on a set with n2 elements coincides with...
research
07/19/2019

Favourite distances in 3-space

Let S be a set of n points in Euclidean 3-space. Assign to each x∈ S a d...
research
08/19/2021

Integrated Random Projection and Dimensionality Reduction by Propagating Light in Photonic Lattices

It is proposed that the propagation of light in disordered photonic latt...
research
03/14/2018

Linearity is Strictly More Powerful than Contiguity for Encoding Graphs

Linearity and contiguity are two parameters devoted to graph encoding. L...
research
02/23/2018

AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction

High dimensionality, i.e. data having a large number of variables, tends...

Please sign up or login with your details

Forgot password? Click here to reset