502 search results - page 12 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
IJCNN
2006
IEEE
14 years 1 months ago
2006
IEEE
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy -- even on parallel processors -- unlike the...
DATASCIENCE
2002
13 years 7 months ago
2002
The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the mic...
APPT
2005
Springer
14 years 1 months ago
2005
Springer
Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clusteri...
BMCBI
2008
13 years 7 months ago
2008
Background: Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differ...