Lossless Prioritized Embeddings

Given metric spaces (X, d) and (Y, ρ) and an ordering x 1 , x 2 , . . . , x n of (X, d), an embedding f : X → Y is said to have a prioritized distortion α(•), for a function α(•), if for any pair x j , x of distinct points in X, the distortion provided by f for this pair is at most α(j). If Y is a normed space, the embedding is said to have prioritized dimension β(•), if f (x j ) may have non-zero entries only in its first β(j) coordinates.

The notion of prioritized embedding was introduced by Filtser and the current authors in [EFN18], where a rather general methodology for constructing such embeddings was developed. Though this methodology enables [EFN18] to come up with many prioritized embeddings, it typically incurs some loss in the distortion. In other words, in the worst-case, prioritized embeddings obtained via this methodology incur distortion which is at least a constant factor off, compared to the distortion of the classical counterparts of these embeddings. This constant loss is problematic for isometric embeddings. It is also troublesome for Matousek's embedding of general metrics into ℓ ∞ , which for a parameter k = 1, 2, . . ., provides distortion 2k -1 and dimension O(k log n • n 1/k ).

In this paper we devise two lossless prioritized embeddings. The first one is an isometric prioritized embedding of tree metrics into ℓ ∞ with dimension O(log j), matching the worst-case guarantee of O(log n) of the classical embedding of Linial et al. [LLR95]. The second one is a prioritized Matousek's embedding of general metrics into ℓ ∞ , which for a parameter k = 1, 2, . . ., provides prioritized distortion 2 k log j log n -1 and dimension O(k log n • n 1/k ), again matching the worst-case guarantee 2k -1 in the distortion of the classical Matousek's embedding.

We also provide a dimension-prioritized variant of Matousek's embedding. Finally, we devise prioritized embeddings of general metrics into (single) ultra-metric and of general graphs into (single) spanning tree with asymptotically optimal distortion.