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» A New Indexing Method for High Dimensional Dataset
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VLDB
2007
ACM
174views Database» more  VLDB 2007»
14 years 5 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
SIGMOD
2009
ACM
235views Database» more  SIGMOD 2009»
14 years 5 months ago
Quality and efficiency in high dimensional nearest neighbor search
Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational data...
Yufei Tao, Ke Yi, Cheng Sheng, Panos Kalnis
VISSYM
2003
13 years 7 months ago
Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets
Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimension...
Jing Yang, Matthew O. Ward, Elke A. Rundensteiner,...
ICASSP
2011
IEEE
13 years 2 months ago
Searching in one billion vectors: re-rank with source coding
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that ...
Hervé Jégou and Romain Tavenard and Matthijs Dou...
ICDE
1997
IEEE
130views Database» more  ICDE 1997»
14 years 7 months ago
High-Dimensional Similarity Joins
Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, calle...
Kyuseok Shim, Ramakrishnan Srikant, Rakesh Agrawal