Error propagation along the different regions of multivariate curve resolution feasible solutions

2017 ◽  
Vol 162 ◽  
pp. 203-213 ◽  
Author(s):  
Mahsa Dadashi ◽  
Hamid Abdollahi ◽  
Romà Tauler
Keyword(s):  
Error Propagation ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Feasible Solutions

Soft-trilinear constraints for improved quantitation in multivariate curve resolution

The Analyst ◽  
2020 ◽  
Vol 145 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Elnaz Tavakkoli ◽  
Hamid Abdollahi ◽  
Paul J. Gemperline
Keyword(s):  
Multivariate Curve Resolution ◽  
True Solution ◽  
Curve Resolution ◽  
Feasible Solutions

Soft trilinearity constraints give a range of feasible solutions (grey) that envelop the true solution (blue). PARAFAC2 (green) and MCR-ALS results (black) are shown for comparison.

Comparative multivariate curve resolution study in the area of feasible solutions

2017 ◽  
Vol 163 ◽  
pp. 55-63 ◽  
Author(s):  
Henning Schröder ◽  
Mathias Sawall ◽  
Christoph Kubis ◽  
Annekathrin Jürß ◽  
Detlef Selent ◽  
...  
Keyword(s):  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Feasible Solutions ◽  
Resolution Study

L1-Norm Sparse Learning and its Application

2011 ◽  
Vol 88-89 ◽  
pp. 379-385 ◽  
Author(s):  
Xin Feng Zhu ◽  
Bin Li ◽  
Jian Dong Wang
Keyword(s):  
Bayesian Learning ◽  
Sparse Representations ◽  
Multivariate Curve Resolution ◽  
Sparse Learning ◽  
Sparse Bayesian Learning ◽  
L1 Norm ◽  
Curve Resolution ◽  
Feasible Solutions ◽  
Sparse Solutions

The need on finding sparse representations has attracted more and more people to research it. Researchers have developed many approaches (such as nonnegative constraint, l1-norm sparsity regularization and sparse Bayesian learning with independent Gaussian prior) for encouraging sparse solutions and established some conditions under which the feasible solutions could be found by those approaches. This paper commbined the L1-norm regularization and bayesian learning, called L1-norm sparse bayesian learning, which was inspired by RVM (relative vector machine). L1-norm sparse bayesian learning has found its applications in many fields such as MCR (multivariate curve resolution) and so on. We proposed a new method called BSMCR (bayesian sparse MCR) to enhance the quality of resolve result.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data
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