Author(s)

Ying Zhu

Mathematics and Mathematics Education, National Institute of Education, Nanyang Technological University, Singapore City, Singapore.

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.

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

Zhu, Y. (2017) Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps. American Journal of Analytical Chemistry, 8, 294-305. doi: 10.4236/ajac.2017.84022.