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» Parallel matrix algorithms and applications
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140
Voted
SDM
2007
SIAM
133views Data Mining» more  SDM 2007»
15 years 5 months ago
Change-Point Detection using Krylov Subspace Learning
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...
Tsuyoshi Idé, Koji Tsuda
127
Voted
IPPS
2002
IEEE
15 years 8 months ago
A Parallel Two-Level Hybrid Method for Diagonal Dominant Tridiagonal Systems
A new method, namely the Parallel Two-Level Hybrid (PTH) method, is developed to solve tridiagonal systems on parallel computers. PTH is designed based on Parallel Diagonal Domina...
Xian-He Sun, Wu Zhang
IPPS
1998
IEEE
15 years 8 months ago
High Performance Data Mining Using Data Cubes on Parallel Computers
On-Line Analytical Processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimens...
Sanjay Goil, Alok N. Choudhary
ICVGIP
2008
15 years 5 months ago
Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application
As vision algorithms mature with increasing inspiration from the learning community, statistically independent pseudo random number generation (PRNG) becomes increasingly importan...
Andrew Janowczyk, Sharat Chandran, Srinivas Aluru
HPCN
1995
Springer
15 years 7 months ago
A hierarchical approach to workload characterization for parallel systems
Performance evaluation studies are to be an integral part of the design and tuning of parallel applications. We propose a hierarchical approach to the systematic characterization o...
Maria Calzarossa, Alessandro P. Merlo, Daniele Tes...