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» Structured metric learning for high dimensional problems
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ICDT
2001
ACM
116views Database» more  ICDT 2001»
14 years 1 days ago
On Optimizing Nearest Neighbor Queries in High-Dimensional Data Spaces
Abstract. Nearest-neighbor queries in high-dimensional space are of high importance in various applications, especially in content-based indexing of multimedia data. For an optimiz...
Stefan Berchtold, Christian Böhm, Daniel A. K...
CVPR
2003
IEEE
14 years 9 months ago
Nearest Neighbor Search for Relevance Feedback
We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback...
Jelena Tesic, B. S. Manjunath
ICASSP
2009
IEEE
14 years 2 months ago
Shrinkage estimation of high dimensional covariance matrices
We address covariance estimation under mean-squared loss in the Gaussian setting. Specifically, we consider shrinkage methods which are suitable for high dimensional problems wit...
Yilun Chen, Ami Wiesel, Alfred O. Hero
PR
2006
116views more  PR 2006»
13 years 7 months ago
Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data
Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the ...
Stefano Rovetta, Francesco Masulli
DAWAK
2005
Springer
14 years 1 months ago
Nearest Neighbor Search on Vertically Partitioned High-Dimensional Data
Abstract. In this paper, we present a new approach to indexing multidimensional data that is particularly suitable for the efficient incremental processing of nearest neighbor quer...
Evangelos Dellis, Bernhard Seeger, Akrivi Vlachou