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SIAMSC
2008
198views more  SIAMSC 2008»
13 years 7 months ago
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
T. Bui-Thanh, Karen Willcox, Omar Ghattas
ICDM
2003
IEEE
178views Data Mining» more  ICDM 2003»
14 years 22 days ago
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dime...
Qi Li, Jieping Ye, Chandra Kambhamettu
ICDE
1997
IEEE
130views Database» more  ICDE 1997»
14 years 8 months ago
High-Dimensional Similarity Joins
Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, calle...
Kyuseok Shim, Ramakrishnan Srikant, Rakesh Agrawal
PAKDD
2010
ACM
173views Data Mining» more  PAKDD 2010»
13 years 5 months ago
Distributed Knowledge Discovery with Non Linear Dimensionality Reduction
Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifol...
Panagis Magdalinos, Michalis Vazirgiannis, Dialect...
GLVLSI
1997
IEEE
110views VLSI» more  GLVLSI 1997»
13 years 11 months ago
Algorithm and Hardware Support for Branch Anticipation
Multi-dimensional systems containing nested loops are widely used to model scientific applications such as image processing, geophysical signal processing and fluid dynamics. Ho...
Ted Zhihong Yu, Edwin Hsing-Mean Sha, Nelson L. Pa...