Supervised scoring model

Teramoto, R. and Fukunishi, H. (2007) Supervised scoring models with docked ligand conformations for structure-based virtual screening. Journal of Chemical Information and Modeling, 47,1858-1867. 

Principle components regression (PCR) is one of the supervised methods commonly employed to analyze NMR data. This method is typically used for developing a quantitative model. In simple terms, PCR can be thought of as PCA followed by a regression step. In PCR, the scores matrix (T) obtained in PCA (Section 3.1) is related to an external variable in a least squares sense. Recall that the data matrix can be reconstructed or estimated using a limited number of factors (/ffact), such that only the fc = Mfaet PCA loadings (l fc) are required to describe the data matrix. Eq. (15) can be reconstructed as... 

Supervised segmentation methods work defining the different pixel classes beforehand with a series of well-identified pixels. Each different class is described by a model and these models are used to assign unknown pixels to the predefined classes. Reference pixels in and out of the classes can be selected by applying class membership masks in the image score scatter plots [74, 75], for instance, or by using results from unsupervised segmentation methods. 

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