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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
14 years 6 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
14 years 6 months ago
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Jieping Ye, Tie Wang
COCOON
1998
Springer
13 years 10 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...
CVPR
2007
IEEE
14 years 7 months ago
The Hierarchical Isometric Self-Organizing Map for Manifold Representation
We present an algorithm, Hierarchical ISOmetric SelfOrganizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimension...
Haiying Guan, Matthew Turk