Sentence examples for multivariate methods such from inspiring English sources

Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA – Target Projection and regularized MANOVA.

Lopez et al. [94] applied the multivariate methods, such as PCA and LDA, to feature extraction, and then employed the Bayesian framework for automated diagnosis of AD and NC using PET and SPECT.

On the other hand, the multivariate methods such as response surface methodology (RS M consider the effect of the variables during the performance simultaneously, which could be a promising view [16].

Database-driven models of peptide binding include multivariate methods such as partial least squares (PLS) and artificial neural networks [6], [7], [8], [9].

Other multivariate methods such as linear discriminant analysis (LDA) [18] or sparse logistic regression (SLR) [16] are also capable of serving as multivariate ranking methods as the SVM.

We detect an increasing number of compounds as technical methods become more sensitive, so finding structure among the two dozen or more compounds detected has often relied on statistical analyses using multivariate methods such as Principal Components Analysis[24].

It is possible that the use of a multivariate method such as Recursive Feature Elimination (De Martino et al. 2008), using the actual classification model, to select voxels could result in a slightly better performance.

Various multivariate methods (unsupervised) such as K-means Cluster Analysis (KMCA), Agglomerative Hierarchical Cluster Analysis (AHCA), Principal Component Analysis (PCA), Fuzzy C Means Cluster Analysis (FCMCA), Vertex Component Analysis (VCA), and Divisive Correlation Cluster Analysis (DCCA) are being widely used for cluster analysis of Raman and IR images [45, 81, 82].

Secondly, feature reduction may change the relationship between samples as determined by multivariate modelling methods such as principal component analysis.

Multivariate analysis methods, such as Clustering analysis and Principle component analysis, were applied to determine the quality differences between samples.

For quantitative calibration, multivariate regression methods such as partial least squares (PLS) [42 44] have been used to develop NIR-based models for characterization of heritage materials extensively.