Doğrusal Olmayan Temel Bileşenler Analizinin Tanıtımı ve Uygulanabilirliği

Nonlinear principal component analysis (NLPCA) is a descriptive dimension reduction method that examines the relationships between variables and displays results numerically visually in multivariate datasets have linear or nonlinear relationship them. In this study, it was aimed to present basic explanatory information about components emphasize its usability by performing application. data obtained from 270 samples for 17 continuous concerning 3 pepper varieties were evaluated Principal (PCA). With 4 as result of PCA, being categorical Variety, storage time Application analyzed NLPCA. made with approximately 74% total variance explained NLPCA, 58% well. As analysis; observed there strong PC1 variety, PC3 PC2 variables, while PC4 application all low. result; examining datasets, these intended be presented an easily interpreted understandable way two-dimensional space; emphasized NLPCA can used alone and/or together other methods.