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SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
15 years 6 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
BMCBI
2004
150views more  BMCBI 2004»
14 years 11 months ago
Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data
Background: A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals ...
Dietmar E. Martin, Philippe Demougin, Michael N. H...
RECSYS
2009
ACM
15 years 6 months ago
Collaborative prediction and ranking with non-random missing data
A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative ï¬...
Benjamin M. Marlin, Richard S. Zemel
BMCBI
2008
128views more  BMCBI 2008»
14 years 12 months ago
Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions
Background: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. ...
Ki-Yeol Kim, Dong Hyuk Ki, Hei-Cheul Jeung, Hyun C...
INTR
2007
120views more  INTR 2007»
14 years 11 months ago
Analysing Google rankings through search engine optimization data
Purpose – The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google. Design/methodology/approach – The paper presents the r...
Michael P. Evans