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A Sequential Algorithm for Multiblock Orthogonal Projections to Latent Structures.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Worley, Bradley Powers, Robert |
| Copyright Year | 2015 |
| Abstract | Methods of multiblock bilinear factorizations have increased in popularity in chemistry and biology as recent increases in the availability of information-rich spectroscopic platforms has made collecting multiple spectroscopic observations per sample a practicable possibility. Of the existing multiblock methods, consensus PCA (CPCA-W) and multiblock PLS (MB-PLS) have been shown to bear desirable qualities for multivariate modeling, most notably their computability from single-block PCA and PLS factorizations. While MB-PLS is a powerful extension to the nonlinear iterative partial least squares (NIPALS) framework, it still spreads predictive information across multiple components when response-uncorrelated variation exists in the data. The OnPLS extension to O2PLS provides a means of simultaneously extracting predictive and uncorrelated variation from a set of matrices, but is more suited to unsupervised data discovery than regression. We describe the union of NIPALS MB-PLS with an orthogonal signal correction (OSC) filter, called MB-OPLS, and illustrate its equivalence to single-block OPLS for regression and discriminant analysis. |
| Starting Page | 33 |
| Ending Page | 39 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.chemolab.2015.10.018 |
| PubMed reference number | 26640310 |
| Journal | Medline |
| Volume Number | 149 |
| Part | B |
| Alternate Webpage(s) | http://bionmr.unl.edu/files/publications/130.pdf |
| Alternate Webpage(s) | http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1064&context=chemistrypowers |
| Journal | Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
