Search and classification of high dimensional data

Yuval Rabani*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageAmerican English
Title of host publicationApproximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings
EditorsKlaus Jansen, Stefano Leonardi, Vijay Vazirani
PublisherSpringer Verlag
Pages1-2
Number of pages2
ISBN (Print)3540441867, 9783540441861
DOIs
StatePublished - 2002
Externally publishedYes
Event5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002 - Rome, Italy
Duration: 17 Sep 200221 Sep 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2462
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002
Country/TerritoryItaly
CityRome
Period17/09/0221/09/02

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

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