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||American English|
|Title of host publication||Approximation Algorithms for Combinatorial Optimization - 5th International Workshop, APPROX 2002, Proceedings|
|Editors||Klaus Jansen, Stefano Leonardi, Vijay Vazirani|
|Number of pages||2|
|ISBN (Print)||3540441867, 9783540441861|
|State||Published - 2002|
|Event||5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002 - Rome, Italy|
Duration: 17 Sep 2002 → 21 Sep 2002
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||5th International Workshop On Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2002|
|Period||17/09/02 → 21/09/02|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.