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BMCBI
2010
144views more  BMCBI 2010»
14 years 9 months ago
Super-sparse principal component analyses for high-throughput genomic data
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
Donghwan Lee, Woojoo Lee, Youngjo Lee, Yudi Pawita...
NN
2000
Springer
159views Neural Networks» more  NN 2000»
14 years 9 months ago
Independent component analysis for noisy data -- MEG data analysis
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such ...
Shiro Ikeda, Keisuke Toyama
NIPS
2004
14 years 11 months ago
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unkno...
Tobias Blaschke, Laurenz Wiskott
EOR
2011
172views more  EOR 2011»
14 years 4 months ago
Efficiency measurement using independent component analysis and data envelopment analysis
Efficiency measurement is an important issue for any firm or organization. Efficiency measurement allows organizations to compare their performance with their competitors’ and t...
Ling-Jing Kao, Chi-Jie Lu, Chih-Chou Chiu
84
Voted
DAC
2006
ACM
15 years 10 months ago
Statistical timing analysis with correlated non-gaussian parameters using independent component analysis
We propose a scalable and efficient parameterized block-based statistical static timing analysis algorithm incorporating both Gaussian and non-Gaussian parameter distributions, ca...
Jaskirat Singh, Sachin S. Sapatnekar