The celebrated Johnson-Lindenstrauss lemma states that for all ε∈(0,1) and finite sets X⊆RN with n>1 elements,
there exists a matrix Φ∈Rm×N with
m=O(ε−2logn) such that (1−ε)∥x−y∥2≤∥Φx−Φy∥2≤(1+ε)∥x−y∥2∀x,y∈X. Herein we consider terminal embedding results which
have recently been introduced in the computer science literature as stronger
extensions of the Johnson-Lindenstrauss lemma for finite sets. After a short
survey of this relatively recent line of work, we extend the theory of terminal
embeddings to hold for arbitrary (e.g., infinite) subsets X⊆RN, and then specialize our generalized results to the case where
X is a low-dimensional compact submanifold of RN. In particular,
we prove the following generalization of the Johnson-Lindenstrauss lemma: For
all ε∈(0,1) and X⊆RN, there exists a
terminal embedding f:RN⟶Rm such that
(1−ε)∥x−y∥2≤∥f(x)−f(y)∥2≤(1+ε)∥x−y∥2∀x∈Xand∀y∈RN. Crucially, we show that the dimension m of the range of
f above is optimal up to multiplicative constants, satisfying
m=O(ε−2ω2(SX)), where ω(SX) is the
Gaussian width of the set of unit secants of X,
SX={(x−y)/∥x−y∥2:x=y∈X}. Furthermore, our
proofs are constructive and yield algorithms for computing a general class of
terminal embeddings f, an instance of which is demonstrated herein to allow
for more accurate compressive nearest neighbor classification than standard
linear Johnson-Lindenstrauss embeddings do in practice.Comment: 16 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:2206.0337