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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
15 years 10 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
95
Voted
APVIS
2010
14 years 4 months ago
Visual analysis of high dimensional point clouds using topological landscapes
In this paper, we present a novel three-stage process to visualize the structure of point clouds in arbitrary dimensions. To get insight into the structure and complexity of a dat...
Patrick Oesterling, Christian Heine, Heike Jä...
UAI
2000
14 years 11 months ago
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
This paper is about the use of metric data structures in high-dimensionalor non-Euclidean space to permit cached sufficientstatisticsaccelerationsof learning algorithms. It has re...
Andrew W. Moore
ICDE
2000
IEEE
168views Database» more  ICDE 2000»
15 years 11 months ago
PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces
In high-dimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object ? can be a very expensive task, because of the poor partitioning operated by...
Paolo Ciaccia, Marco Patella
101
Voted
CGF
2011
14 years 1 months ago
Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis
High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of th...
Bilkis J. Ferdosi, Jos B. T. M. Roerdink