On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Subsets of RN\mathbb{R}^N

Abstract

The celebrated Johnson-Lindenstrauss lemma states that for all ε(0,1)\varepsilon \in (0,1) and finite sets XRNX \subseteq \mathbb{R}^N with n>1n>1 elements, there exists a matrix ΦRm×N\Phi \in \mathbb{R}^{m \times N} with m=O(ε2logn)m=\mathcal{O}(\varepsilon^{-2}\log n) such that (1ε)xy2ΦxΦy2(1+ε)xy2x,yX. (1 - \varepsilon) \|x-y\|_2 \leq \|\Phi x-\Phi y\|_2 \leq (1+\varepsilon)\| x- y\|_2 \quad \forall\, x, y \in 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 XRNX \subseteq \mathbb{R}^N, and then specialize our generalized results to the case where XX is a low-dimensional compact submanifold of RN\mathbb{R}^N. In particular, we prove the following generalization of the Johnson-Lindenstrauss lemma: For all ε(0,1)\varepsilon \in (0,1) and XRNX\subseteq\mathbb{R}^N, there exists a terminal embedding f:RNRmf: \mathbb{R}^N \longrightarrow \mathbb{R}^{m} such that (1ε)xy2f(x)f(y)2(1+ε)xy2xX and yRN.(1 - \varepsilon) \| x - y \|_2 \leq \left\| f(x) - f(y) \right\|_2 \leq (1 + \varepsilon) \| x - y \|_2 \quad \forall \, x \in X ~{\rm and}~ \forall \, y \in \mathbb{R}^N. Crucially, we show that the dimension mm of the range of ff above is optimal up to multiplicative constants, satisfying m=O(ε2ω2(SX))m=\mathcal{O}(\varepsilon^{-2} \omega^2(S_X)), where ω(SX)\omega(S_X) is the Gaussian width of the set of unit secants of XX, SX={(xy)/xy2 ⁣:xyX}S_X=\overline{\{(x-y)/\|x-y\|_2 \colon x \neq y \in X\}}. Furthermore, our proofs are constructive and yield algorithms for computing a general class of terminal embeddings ff, 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

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