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ICDE
2008
IEEE
158views Database» more  ICDE 2008»
16 years 7 months ago
CARE: Finding Local Linear Correlations in High Dimensional Data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selec...
Xiang Zhang, Feng Pan, Wei Wang
IJCAI
2003
15 years 7 months ago
Continuous nonlinear dimensionality reduction by kernel Eigenmaps
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
Matthew Brand
SODA
2000
ACM
127views Algorithms» more  SODA 2000»
15 years 7 months ago
Dimensionality reduction techniques for proximity problems
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
Piotr Indyk
SIGMOD
2001
ACM
193views Database» more  SIGMOD 2001»
16 years 6 months ago
Epsilon Grid Order: An Algorithm for the Similarity Join on Massive High-Dimensional Data
The similarity join is an important database primitive which has been successfully applied to speed up applications such as similarity search, data analysis and data mining. The s...
Christian Böhm, Bernhard Braunmüller, Fl...
CGO
2007
IEEE
16 years 18 days ago
Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time
Emerging microprocessors offer unprecedented parallel computing capabilities and deeper memory hierarchies, increasing the importance of loop transformations in optimizing compile...
Louis-Noël Pouchet, Cédric Bastoul, Al...