Additive Approximation Schemes for Low-Dimensional Embeddings

We consider the task of fitting low-dimensional embeddings to high-dimensional data. In particular, we study the $k$-Euclidean Metric Violation problem (\textsf{k-EMV}), where the input is $D \in \mathbb{R}^{\binom{n}{2}}_{\geq 0}$ and the goal is to find the closest vector $X \in \mathbb{M}_{k}$, where $\mathbb{M}_k \subset \mathbb{R}^{\binom{n}{2}}_{\geq 0}$ is the set of all $k$-dimensional Euclidean metrics on $n$ points, and closeness is formulated as the following optimization problem, where $\| \cdot \|$ is the entry-wise $\ell_2$ norm: $$\textsf{OPT}_{\textrm{EMV}} = \min_{X \in \mathbb{M}_{k} } \Vert D - X \Vert_2^2\,.$$ Cayton and Dasgupta [CD'06] showed that this problem is NP-Hard, even when $k=1$. Dhamdhere [Dha'04] obtained a $O(\log(n))$-approximation for \textsf{1-EMV} and leaves finding a PTAS for it as an open question (reiterated recently by Lee [Lee'25]). Although \textsf{k-EMV} has been studied in the statistics community for over 70 years, under the name "multi-dimensional scaling", there are no known efficient approximation algorithms for $k > 1$, to the best of our knowledge. We provide the first polynomial-time additive approximation scheme for \textsf{k-EMV}. In particular, we obtain an embedding with objective value $\textsf{OPT}_{\textrm{EMV}} + \varepsilon \Vert D\Vert_2^2$ in $(n\cdot B)^{\mathsf{poly}(k, \varepsilon^{-1})}$ time, where each entry in $D$ can be represented by $B$ bits. We believe our algorithm is a crucial first step towards obtaining a PTAS for \textsf{k-EMV}. Our key technical contribution is a new analysis of correlation rounding for Sherali-Adams / Sum-of-Squares relaxations, tailored to low-dimensional embeddings. We also show that our techniques allow us to obtain additive approximation schemes for two related problems: a weighted variant of \textsf{k-EMV} and $\ell_p$ low-rank approximation for $p>2$.