Partial least squares discriminant data classification

Figure 13.3-5. Partial least-squares discriminant analysis model for the classification of blood plasma samples in terms of coronary artery disease, based on their NMR spectra with visualization of the degree of coronary artery occlusion. Each point is based on data from a NMR spectrum of human blood plasma from subjects with different degrees of coronary artery occlusion. Circles—no stenosis, triangles—stenosis of one artery, inverted triangles—stenosis of two arteries, and squares—stenosis of three arteries. (This figure is available in full color at ftp //ftp.wiley.com/public/sci tech med/pharmaceutical biotech/.)...

Romisch et al. in 2009 presented a study on the characterization and determination of the geographical origin of wines. In this paper, three methods of discrimination and classification of multivariate data were considered and tested the classification and regression trees (CART), the regularized discriminant analysis (RDA) and the partial least squares discriminant analysis (PLS-DA). PLS-DA analysis showed better classification results with percentage of correct classified samples from 88 to 100%. 

Tan, Y. X., Shi, L. B Tong, W. D Hwang, G. T. G., Wang, C. (2004). Multiclass tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Compu. Biol. Chemis. 28(3), 235-244. 

The methods of data analysis depend on the nature of the final output. If the problem is one of classification, a number of multivariate classifiers are available such as those based on principal components analysis (SIMCA), cluster analysis and discriminant analysis, or non-linear artificial neural networks. If the required output is a continuous variable, such as a concentration, then partial least squares regression or principal component regression are often used [20]. 

To establish a correlation between the concentrations of different kinds of nucleosides in a complex metabolic system and normal or abnormal states of human bodies, computer-aided pattern recognition methods are required (15, 16). Different kinds of pattern recognition methods based on multivariate data analysis such as principal component analysis (PCA) (8), partial least squares (16), stepwise discriminant analysis, and canonical discriminant analysis (10, 11) have been reported. Linear discriminant analysis (17, 18) and cluster analysis were also investigated (19,20). Artificial neural network (ANN) is a branch of chemometrics that resolves regression or classification problems. The applications of ANN in separation science and chemistry have been reported widely (21-23). For pattern recognition analysis in clinical study, ANN was also proven to be a promising method (8). 

Supervised methods for recognizing patterns can also be based on multivariate modeling methods, for example, by use of PLS as discussed in Section 6.2.2. The method is termed discriminant analysis-partial least squares (DA-PLS) analysis where the input feature data from the X matrix and the assignment to a class is described in the Y matrix. To avoid a ranking of classes, the containment of classes is not coded in a single classification vector, for example, classes 1-6, but is described by ones or zeros columnwise in the Y matrix. 

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