Search and classification of high dimensional data

Yuval Rabani

doi:10.1007/3-540-45753-4_1

Search and classification of high dimensional data

Yuval Rabani^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Modeling data sets as points in a high dimensional vector space is a trendy theme in modern information retrieval and data mining. Among the numerous drawbacks of this approach is the fact that many of the required processing tasks are computationally hard in high dimension. We survey several algorithmic ideas that have applications to the design and analysis of polynomial time approximation schemes for nearest neighbor search and clustering of high dimensional data. The main lesson from this line of research is that if one is willing to settle for approximate solutions, then high dimensional geometry is easy. Examples are included in the reference list below.

Original language	English
Title of host publication	Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings
Editors	Klaus Jansen, Stefano Leonardi, Vijay Vazirani
Publisher	Springer Verlag
Pages	1-2
Number of pages	2
ISBN (Print)	3540441867, 9783540441861
DOIs	https://doi.org/10.1007/3-540-45753-4_1
State	Published - 2002
Externally published	Yes
Event	5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002 - Rome, Italy Duration: 17 Sep 2002 → 21 Sep 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2462
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002
Country/Territory	Italy
City	Rome
Period	17/09/02 → 21/09/02

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

Access to Document

10.1007/3-540-45753-4_1

Cite this

Rabani, Y. (2002). Search and classification of high dimensional data. In K. Jansen, S. Leonardi, & V. Vazirani (Eds.), Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings (pp. 1-2). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2462). Springer Verlag. https://doi.org/10.1007/3-540-45753-4_1

Rabani, Yuval. / Search and classification of high dimensional data. Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings. editor / Klaus Jansen ; Stefano Leonardi ; Vijay Vazirani. Springer Verlag, 2002. pp. 1-2 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1873ffd925c149efb090a4503c2f21d7,

title = "Search and classification of high dimensional data",

abstract = "Modeling data sets as points in a high dimensional vector space is a trendy theme in modern information retrieval and data mining. Among the numerous drawbacks of this approach is the fact that many of the required processing tasks are computationally hard in high dimension. We survey several algorithmic ideas that have applications to the design and analysis of polynomial time approximation schemes for nearest neighbor search and clustering of high dimensional data. The main lesson from this line of research is that if one is willing to settle for approximate solutions, then high dimensional geometry is easy. Examples are included in the reference list below.",

author = "Yuval Rabani",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002 ; Conference date: 17-09-2002 Through 21-09-2002",

year = "2002",

doi = "10.1007/3-540-45753-4_1",

language = "אנגלית",

isbn = "3540441867",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1--2",

editor = "Klaus Jansen and Stefano Leonardi and Vijay Vazirani",

booktitle = "Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings",

address = "גרמניה",

}

Rabani, Y 2002, Search and classification of high dimensional data. in K Jansen, S Leonardi & V Vazirani (eds), Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2462, Springer Verlag, pp. 1-2, 5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002, Rome, Italy, 17/09/02. https://doi.org/10.1007/3-540-45753-4_1

Search and classification of high dimensional data. / Rabani, Yuval.
Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings. ed. / Klaus Jansen; Stefano Leonardi; Vijay Vazirani. Springer Verlag, 2002. p. 1-2 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2462).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Search and classification of high dimensional data

AU - Rabani, Yuval

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2002.

PY - 2002

Y1 - 2002

N2 - Modeling data sets as points in a high dimensional vector space is a trendy theme in modern information retrieval and data mining. Among the numerous drawbacks of this approach is the fact that many of the required processing tasks are computationally hard in high dimension. We survey several algorithmic ideas that have applications to the design and analysis of polynomial time approximation schemes for nearest neighbor search and clustering of high dimensional data. The main lesson from this line of research is that if one is willing to settle for approximate solutions, then high dimensional geometry is easy. Examples are included in the reference list below.

AB - Modeling data sets as points in a high dimensional vector space is a trendy theme in modern information retrieval and data mining. Among the numerous drawbacks of this approach is the fact that many of the required processing tasks are computationally hard in high dimension. We survey several algorithmic ideas that have applications to the design and analysis of polynomial time approximation schemes for nearest neighbor search and clustering of high dimensional data. The main lesson from this line of research is that if one is willing to settle for approximate solutions, then high dimensional geometry is easy. Examples are included in the reference list below.

UR - http://www.scopus.com/inward/record.url?scp=84957026516&partnerID=8YFLogxK

U2 - 10.1007/3-540-45753-4_1

DO - 10.1007/3-540-45753-4_1

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84957026516

SN - 3540441867

SN - 9783540441861

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 2

BT - Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings

A2 - Jansen, Klaus

A2 - Leonardi, Stefano

A2 - Vazirani, Vijay

PB - Springer Verlag

T2 - 5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002

Y2 - 17 September 2002 through 21 September 2002

ER -

Rabani Y. Search and classification of high dimensional data. In Jansen K, Leonardi S, Vazirani V, editors, Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings. Springer Verlag. 2002. p. 1-2. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45753-4_1

Search and classification of high dimensional data

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this