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ICDE
2003
IEEE
116views Database» more  ICDE 2003»
16 years 6 months ago
Joining Massive High-Dimensional Datasets
We consider the problem of joining massive datasets. We propose two techniques for minimizing disk I/O cost of join operations for both spatial and sequence data. Our techniques o...
Tamer Kahveci, Christian A. Lang, Ambuj K. Singh
SDM
2008
SIAM
123views Data Mining» more  SDM 2008»
15 years 6 months ago
Constrained Co-clustering of Gene Expression Data
In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a collection...
Ruggero G. Pensa, Jean-François Boulicaut
159
Voted
ICCV
2009
IEEE
15 years 2 months ago
Learning with dynamic group sparsity
This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
Junzhou Huang, Xiaolei Huang, Dimitris N. Metaxas
134
Voted
JISE
2010
144views more  JISE 2010»
14 years 11 months ago
Variant Methods of Reduced Set Selection for Reduced Support Vector Machines
In dealing with large datasets the reduced support vector machine (RSVM) was proposed for the practical objective to overcome the computational difficulties as well as to reduce t...
Li-Jen Chien, Chien-Chung Chang, Yuh-Jye Lee
IPPS
2006
IEEE
15 years 10 months ago
MegaProto/E: power-aware high-performance cluster with commodity technology
In our research project named “Mega-Scale Computing Based on Low-Power Technology and Workload Modeling”, we have been developing a prototype cluster not based on ASIC or FPGA...
Taisuke Boku, Mitsuhisa Sato, Daisuke Takahashi, H...