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The problem of computing a bi-Lipschitz embedding of a graphical metric into the line with minimum distortion has received a lot of attention. The best-known approximation algorithm computes an embedding with distortion $O(c^2)$, where $c$ denotes the optimal distortion [Bu{a}doiu etal~2005]. We present a bi-criteria approximation algorithm that extends the above results to the setting of emph{outliers}. Specifically, we say that a metric space $(X,rho)$ admits a $(k,c)$-embedding if there exists $Ksubset X$, with $|K|=k$, such that $(Xsetminus K, rho)$ admits an embedding into the line with distortion at most $c$. Given $kgeq 0$, and a metric space that admits a $(k,c)$-embedding, for some $cgeq 1$, our algorithm computes a $({mathsf p}{mathsf o}{mathsf l}{mathsf y}(k, c, log n), {mathsf p}{mathsf o}{mathsf l}{mathsf y}(c))$-embedding in polynomial time. This is the first algorithmic result for outlier bi-Lipschitz embeddings. Prior to our work, comparable outlier embeddings where known only for the case of additive distortion.
We propose a dimensionality reducing matrix design based on training data with constraints on its Frobenius norm and number of rows. Our design criteria is aimed at preserving the distances between the data points in the dimensionality reduced space
A quasiconformal tree is a doubling metric tree in which the diameter of each arc is bounded above by a fixed multiple of the distance between its endpoints. In this paper we show that every quasiconformal tree bi-Lipschitz embeds in some Euclidean s
Let H be a graph, and let C_H(G) be the number of (subgraph isomorphic) copies of H contained in a graph G. We investigate the fundamental problem of estimating C_H(G). Previous results cover only a few specific instances of this general problem, for
In this article we start a systematic study of the bi-Lipschitz geometry of lamplighter graphs. We prove that lamplighter graphs over trees bi-Lipschitzly embed into Hamming cubes with distortion at most~$6$. It follows that lamplighter graphs over c
We develop efficient algorithms for estimating low-degree moments of unknown distributions in the presence of adversarial outliers. The guarantees of our algorithms improve in many cases significantly over the best previous ones, obtained in recent w