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» Parallel Algorithms for Singular Value Decomposition
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SIAMMAX
2011
141views more  SIAMMAX 2011»
14 years 13 days ago
Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme
Higher-order tensors are used in many application fields, such as statistics, signal processing, and scientific computing. Efficient and reliable algorithms for manipulating thes...
Mariya Ishteva, Pierre-Antoine Absil, Sabine Van H...
TSP
2008
178views more  TSP 2008»
14 years 9 months ago
Heteroscedastic Low-Rank Matrix Approximation by the Wiberg Algorithm
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...
Pei Chen
CVPR
2010
IEEE
14 years 7 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
SIGIR
2011
ACM
14 years 13 days ago
Utilizing marginal net utility for recommendation in e-commerce
Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a ...
Jian Wang, Yi Zhang
PKDD
2004
Springer
138views Data Mining» more  PKDD 2004»
15 years 3 months ago
Combining Multiple Clustering Systems
Three methods for combining multiple clustering systems are presented and evaluated, focusing on the problem of finding the correspondence between clusters of different systems. ...
Constantinos Boulis, Mari Ostendorf