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TSP
2008
178views more  TSP 2008»
13 years 7 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
SIGIR
2009
ACM
14 years 2 months ago
Fast nonparametric matrix factorization for large-scale collaborative filtering
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gon...
FPL
2005
Springer
149views Hardware» more  FPL 2005»
14 years 1 months ago
Heterogeneity Exploration for Multiple 2D Filter Designs
Many image processing applications require fast convolution of an image with a set of large 2D filters. Field - Programmable Gate Arrays (FPGAs) are often used to achieve this go...
Christos-Savvas Bouganis, Peter Y. K. Cheung, Geor...
DEBU
2008
165views more  DEBU 2008»
13 years 7 months ago
A Survey of Attack-Resistant Collaborative Filtering Algorithms
With the increasing popularity of recommender systems in commercial services, the quality of recommendations has increasingly become an important to study, much like the quality o...
Bhaskar Mehta, Thomas Hofmann
CVPR
2010
IEEE
13 years 5 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