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» Structured metric learning for high dimensional problems
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COCOON
1998
Springer
13 years 9 months ago
The Colored Sector Search Tree: A Dynamic Data Structure for Efficient High Dimensional Nearest-Foreign-Neighbor Queries
Abstract. In this paper we present the new data structure Colored Sector Search Tree (CSST ) for solving the Nearest-Foreign-Neighbor Query Problem (NFNQP ): Given a set S of n col...
Thomas Graf, V. Kamakoti, N. S. Janaki Latha, C. P...
PAMI
2006
117views more  PAMI 2006»
13 years 4 months ago
Metric Learning for Text Documents
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from ...
Guy Lebanon
IJCAI
1997
13 years 6 months ago
Is Nonparametric Learning Practical in Very High Dimensional Spaces?
Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and ...
Gregory Z. Grudic, Peter D. Lawrence

Publication
417views
14 years 1 months ago
Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces
We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximate...
Peter N. Yianilos
PAKDD
2009
ACM
186views Data Mining» more  PAKDD 2009»
13 years 11 months ago
Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles