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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
14 years 8 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
TKDE
2011
332views more  TKDE 2011»
13 years 2 months ago
Adaptive Cluster Distance Bounding for High-Dimensional Indexing
—We consider approaches for similarity search in correlated, high-dimensional data-sets, which are derived within a clustering framework. We note that indexing by “vector appro...
Sharadh Ramaswamy, Kenneth Rose
CEC
2009
IEEE
14 years 2 months ago
A clustering particle swarm optimizer for dynamic optimization
Abstract—In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal soluti...
Changhe Li, Shengxiang Yang
ICC
2009
IEEE
151views Communications» more  ICC 2009»
14 years 2 months ago
Improved Vector Perturbation with Modulo Loss Reduction for Multiuser Downlink Systems
— In this paper, we present an improved precoding technique which reduces a modulo loss in vector perturbation (VP) with low complexity for the downlink of a multiuser multiple-i...
Hyeon-Seung Han, Seokhwan Park, Inkyu Lee
SDM
2009
SIAM
170views Data Mining» more  SDM 2009»
14 years 5 months ago
Optimal Distance Bounds on Time-Series Data.
Most data mining operations include an integral search component at their core. For example, the performance of similarity search or classification based on Nearest Neighbors is ...
Michail Vlachos, Philip S. Yu, Suleyman S. Kozat