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VLDB
2000
ACM
229views Database» more  VLDB 2000»
11 years 9 months ago
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...
Kaushik Chakrabarti, Sharad Mehrotra
ICDE
2007
IEEE
228views Database» more  ICDE 2007»
11 years 12 months ago
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the “curse of dimensionality”. In order to speed up the retrieval efficiency, p...
Xiang Lian, Lei Chen 0002
SIGMOD
2001
ACM
184views Database» more  SIGMOD 2001»
12 years 5 months ago
Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data....
Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehro...
ICDE
1998
IEEE
163views Database» more  ICDE 1998»
12 years 7 months ago
Fast Nearest Neighbor Search in High-Dimensional Space
Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query process...
Stefan Berchtold, Bernhard Ertl, Daniel A. Keim, H...
PODS
2001
ACM
190views Database» more  PODS 2001»
12 years 5 months ago
On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
Charu C. Aggarwal
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