Nearest-Neighbor Graphs

نویسنده

  • Steven Finch
چکیده

Consider a set  of  points that are independently and uniformly distributed in the -dimensional unit cube. Let  ∈  . There exists almost-surely  ∈  such that  6=  and | − |  | − | for all  ∈  ,  6= ,  6= . The point  is called the nearest neighbor of  and we write  ≺ . Note that  ≺  does not imply  ≺ . Draw an edge connecting  and  if and only if  ≺ ; the resulting graph of  vertices and ≤  edges is called the nearest-neighbor graph  on  . What is the probability, (), given  ∈  , that  ≺  implies  ≺ ? Such a pair is isolated from the rest of , in the sense that the only edge touching  or  is the edge that connects  and . We have [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using the Mutual k-Nearest Neighbor Graphs for Semi-supervised Classification on Natural Language Data

The first step in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neighbor graphs for high-dimensional natural language data. To compare the performance of these two graph construction methods, we ru...

متن کامل

Parallel Construction of k-Nearest Neighbor Graphs for Point Clouds

We present a parallel algorithm for k-nearest neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existing methods: (1) Faster construction of k-nearest neighbor graphs in practice on multi-core machines. (2) Less space usage. (3) Better cache efficiency. (4) Ability to handle large data sets. (5) Ease of parallelization an...

متن کامل

The Diameter of Nearest Neighbor Graphs

Any connected plane nearest neighbor graph has diameter Ω(n). This bound generalizes to Ω(n) in any dimension d. For any set of n points in the plane, we define the nearest neighbor graph by selecting a unique nearest neighbor for each point, and adding an edge between each point and its neighbor. This is a directed graph with outdegree one; thus it is a pseudo-forest. Each component of the pse...

متن کامل

Cluster Identification in Nearest-Neighbor Graphs

Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are “identified”: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds ...

متن کامل

Fast Large-Scale Approximate Graph Construction for NLP

Many natural language processing problems involve constructing large nearest-neighbor graphs. We propose a system called FLAG to construct such graphs approximately from large data sets. To handle the large amount of data, our algorithm maintains approximate counts based on sketching algorithms. To find the approximate nearest neighbors, our algorithm pairs a new distributed online-PMI algorith...

متن کامل

Random nearest neighbor and influence graphs on Zd

Random nearest neighbor and influence graphs with vertex set Zd are defined and their percolation properties are studied. The nearest neighbor graph has (with probability 1) only finite connected components and a superexponentially decaying connectivity function. Influence graphs (which are related to energy minimization searches in disordered Ising models) have a percolation transition. © 1999...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008