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» Concept Lattice Reduction by Singular Value Decomposition
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CORR
2010
Springer
189views Education» more  CORR 2010»
13 years 7 months ago
Robust PCA via Outlier Pursuit
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it ...
Huan Xu, Constantine Caramanis, Sujay Sanghavi
BCI
2009
IEEE
14 years 3 months ago
On the Performance of SVD-Based Algorithms for Collaborative Filtering
—In this paper, we describe and compare three Collaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implemen...
Manolis G. Vozalis, Angelos I. Markos, Konstantino...
ICPR
2004
IEEE
14 years 9 months ago
Missing Microarray Data Estimation Based on Projection onto Convex Sets Method
DNA microarrays have gained widespread uses in biological studies. Missing values in a microarray experiment must be estimated before further analysis. In this paper, we propose a...
Alan Wee-Chung Liew, Hong Yan, Xiangchao Gan
WCRE
2008
IEEE
14 years 2 months ago
Automated Concept Location Using Independent Component Analysis
Concept location techniques are designed to help isolate sections of source code that relate to specific concepts. Blind Signal Separation techniques like Singular Value Decompos...
Scott Grant, James R. Cordy, David B. Skillicorn
SIGIR
1995
ACM
14 years 3 days ago
Noise Reduction in a Statistical Approach to Text Categorization
This paper studies noise reduction for computational efficiency improvements in a statistical learning method for text categorization, the Linear Least Squares Fit (LLSF) mapping...
Yiming Yang