Aguilera, A.M., Escabias, M., Preda, C. and Saporta, G. (2010) Using Basis Expansions for Estimating Functional PLS Regression: Applications with Chemometric Data. Chemometrics and Intelligent Laboratory Systems, 104, 289-305.

has been cited by the following article:

• TITLE: Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

  AUTHORS: Ying Zhu

  KEYWORDS: Functional Principal Component Analysis, Functional Partial Least Squares, Functional Logistic Regression, Principal Component Discriminant Analysis, Partial Least Squares Discriminant Analysis

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.8 No.4, April 30, 2017

  ABSTRACT: In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.