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» Nonlinear principal component analysis of noisy data
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BMCBI
2010
277views more  BMCBI 2010»
14 years 9 months ago
PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms
Background: Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless,...
Marc Bruckskotten, Mario Looso, Franz Cemic, Anne ...
IJCNN
2007
IEEE
15 years 3 months ago
Neural Network Ensembles for Time Series Prediction
— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predict...
Dymitr Ruta, Bogdan Gabrys
TIP
2002
179views more  TIP 2002»
14 years 9 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki
VISSYM
2007
15 years 7 hour ago
Parametric Visualization of High Resolution Correlated Multi-spectral Features Using PCA
An imaging mass spectrometer is an analytical instrument that can determine the spatial distribution of chemical compounds on complex surfaces. The output of the device is a multi...
Alexander Broersen, Robert van Liere, Ron M. A. He...
CCE
2007
14 years 9 months ago
A systematic approach for soft sensor development
This paper presents a systematic approach based on robust statistical techniques for development of a data-driven soft sensor, which is an important component of the process analy...
Bao Lin, Bodil Recke, Jørgen K. H. Knudsen,...