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PDCN
2004
15 years 14 days ago
K-Means VQ algorithm using a low-cost parallel cluster computing
It is well-known that the time and memory necessary to create a codebook from large training databases have hindered the vector quantization based systems for real applications. T...
Paulo Sergio Lopes de Souza, Alceu de Souza Britto...
EGPGV
2004
Springer
143views Visualization» more  EGPGV 2004»
15 years 4 months ago
Massive Data Pre-Processing with a Cluster Based Approach
Data coming from complex simulation models reach easily dimensions much greater than available computational resources. Visualization of such data still represents the most intuit...
Rita Borgo, Valerio Pascucci, Roberto Scopigno
CCGRID
2001
IEEE
15 years 2 months ago
A DSM Cluster Architecture Supporting Aggressive Computation in Active Networks
Active networks allow computations to be performed innetwork at routers as messages pass through them. Active networks offer unique opportunities to optimize networkcentric applic...
Peter C. J. Graham
82
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ICDAR
2003
IEEE
15 years 4 months ago
A Low-Cost Parallel K-Means VQ Algorithm Using Cluster Computing
In this paper we propose a parallel approach for the Kmeans Vector Quantization (VQ) algorithm used in a twostage Hidden Markov Model (HMM)-based system for recognizing handwritte...
Alceu de Souza Britto Jr., Paulo Sergio Lopes de S...
ICML
2009
IEEE
15 years 12 months ago
Multi-view clustering via canonical correlation analysis
Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by proje...
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu,...