2001
DOI: 10.1145/502807.502808
|View full text |Cite
|
Sign up to set email alerts
|

Searching in metric spaces

Abstract: The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Be… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

5
773
0
91

Year Published

2004
2004
2020
2020

Publication Types

Select...
5
3
2

Relationship

1
9

Authors

Journals

citations
Cited by 1,006 publications
(869 citation statements)
references
References 48 publications
5
773
0
91
Order By: Relevance
“…Chavez et. al [29] present a survey of techniques devoted to the nearest neighbors search, splitting them into two groups: pivot based and cluster based algorithms. On the former, instances are selected as pivots, avoiding some distance calculations.…”
Section: Nearest Neighbors Searchmentioning
confidence: 99%
“…Chavez et. al [29] present a survey of techniques devoted to the nearest neighbors search, splitting them into two groups: pivot based and cluster based algorithms. On the former, instances are selected as pivots, avoiding some distance calculations.…”
Section: Nearest Neighbors Searchmentioning
confidence: 99%
“…. [2], although, among them, when distances have a high computational cost, methods based on AESA stand out. That is precisely our case, since our distance is the CED.…”
Section: Aesa and Laesamentioning
confidence: 99%
“…In R D with points chosen randomly, the dimension is simply D. In metric spaces or in R D where points are not uniformly distributed, the dimension can be defined using distance histogram properties [8]. In general terms, the dimension grows as the histogram concentrates.…”
Section: A Summary Of Metric Spacesmentioning
confidence: 99%