8 Low-distortion Embeddings of Finite Metric Spaces
نویسندگان
چکیده
An n-point metric space (X,D) can be represented by an n × n table specifying the distances. Such tables arise in many diverse areas. For example, consider the following scenario in microbiology: X is a collection of bacterial strains, and for every two strains, one is given their dissimilarity (computed, say, by comparing their DNA). It is difficult to see any structure in a large table of numbers, and so we would like to represent a given metric space in a more comprehensible way. For example, it would be very nice if we could assign to each x ∈ X a point f(x) in the plane in such a way that D(x, y) equals the Euclidean distance of f(x) and f(y). Such a representation would allow us to see the structure of the metric space: tight clusters, isolated points, and so on. Another advantage would be that the metric would now be represented by only 2n real numbers, the coordinates of the n points in the plane, instead of ( n 2 )
منابع مشابه
Multiembedding of Metric Spaces
Metric embedding has become a common technique in the design of algorithms. Its applicability is often dependent on how high the embedding’s distortion is. For example, embedding finite metric space into trees may require linear distortion as a function of its size. Using probabilistic metric embeddings, the bound on the distortion reduces to logarithmic in the size. We make a step in the direc...
متن کاملLow dimensional embeddings of ultrametrics
In this note we show that every n-point ultrametric embeds with constant distortion in l O(logn) p for every ∞ ≥ p ≥ 1. More precisely, we consider a special type of ultrametric with hierarchical structure called a k-hierarchically well-separated tree (k-HST). We show that any k-HST can be embedded with distortion at most 1 + O(1/k) in l O(k2 logn) p . These facts have implications to embedding...
متن کاملFinite Metric Spaces & Their Embeddings: Introduction and Basic Tools
Definition of (semi) metric. CS motivation. Finite metric spaces arise naturally in combinatorial objects, and algo-rithmic questions. For example, as the shortest path metrics on graphs. We will also see less obvious connections. Properties of finite metrics. The following properties have been investigated: Dimension , extendability of Lipschitz and Hölder functions, decomposability, Inequalit...
متن کاملOn Low Distortion Embeddings of Statistical Distance Measures into Low Dimensional Spaces
Statistical distance measures have found wide applicability in information retrieval tasks that typically involve high dimensional datasets. In order to reduce the storage space and ensure efficient performance of queries, dimensionality reduction while preserving the inter-point similarity is highly desirable. In this paper, we investigate various statistical distance measures from the point o...
متن کاملA Novel Approach to Embedding of Metric Spaces
An embedding of one metric space (X, d) into another (Y, ρ) is an injective map f : X → Y . The central genre of problems in the area of metric embedding is finding such maps in which the distances between points do not change “too much”. Metric Embedding plays an important role in a vast range of application areas such as computer vision, computational biology, machine learning, networking, st...
متن کاملLeast-Distortion Euclidean Embeddings of Graphs: Products of Cycles and Expanders
Embeddings of finite metric spaces into Euclidean space have been studied in several contexts: The local theory of Banach spaces, the design of approximation algorithms, and graph theory. The emphasis is usually on embeddings with the least possible distortion. That is, one seeks an embedding that minimizes the bi-Lipschitz constant of the mapping. This question has also been asked for embeddin...
متن کامل