Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.Facultad de Informátic
This paper presents a novel approach to perform fast approximate nearest neighbors search in high di...
In real world, there are billions of rows in a spatial database. If someone want to search for a loc...
Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning dat...
Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Grap...
Given a collection of objects in a metric space, the Nearest Neighbor Graph (NNG) associate each nod...
Among the similarity queries in metric spaces, there are one that obtains the k-nearest neighbors of...
University of Minnesota Ph.D. dissertation.June 2016. Major: Computer Science. Advisor: George Kary...
Artículo de publicación ISIProximity searching is the problem of retrieving,from a given database, t...
The nearest neighbor graph is an important structure in many data mining methods for clustering, adv...
Proximity searching is the problem of retrieving, from a given database, those objects closest to a ...
We investigate the problem of approximate Nearest-Neighbor Search (NNS) in graphical metrics: The ta...
23 pages (this version is cleaner that the previous report and includes empirical results for the Sm...
AbstractThe nearest neighbor search (NNS) problem is the following: Given a set of n points P={p1, …...
We introduce a new nearest neighbor search al-gorithm. The algorithm builds a nearest neighbor graph...
The nearest neighbour problem is of practical significance in a number of fields. Often we are inter...
This paper presents a novel approach to perform fast approximate nearest neighbors search in high di...
In real world, there are billions of rows in a spatial database. If someone want to search for a loc...
Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning dat...
Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Grap...
Given a collection of objects in a metric space, the Nearest Neighbor Graph (NNG) associate each nod...
Among the similarity queries in metric spaces, there are one that obtains the k-nearest neighbors of...
University of Minnesota Ph.D. dissertation.June 2016. Major: Computer Science. Advisor: George Kary...
Artículo de publicación ISIProximity searching is the problem of retrieving,from a given database, t...
The nearest neighbor graph is an important structure in many data mining methods for clustering, adv...
Proximity searching is the problem of retrieving, from a given database, those objects closest to a ...
We investigate the problem of approximate Nearest-Neighbor Search (NNS) in graphical metrics: The ta...
23 pages (this version is cleaner that the previous report and includes empirical results for the Sm...
AbstractThe nearest neighbor search (NNS) problem is the following: Given a set of n points P={p1, …...
We introduce a new nearest neighbor search al-gorithm. The algorithm builds a nearest neighbor graph...
The nearest neighbour problem is of practical significance in a number of fields. Often we are inter...
This paper presents a novel approach to perform fast approximate nearest neighbors search in high di...
In real world, there are billions of rows in a spatial database. If someone want to search for a loc...
Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning dat...