Doğrusal Olmayan Temel Bileşenler Analizinin Tanıtımı ve Uygulanabilirliği

Abstract

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

The Effect of Funneliformis mosseae and Biochar Against Verticillium dahliae in Pepper Plants under Salt Stress

“…This demonstrates the complex effects of different factors on plant growth. Furthermore, these analyses can help in the summarization and interpretation of variables (Demir et al, 2021). Yavuz et al (2020) also found that PCA is a useful technique to assess sensitivity to stressful conditions.…”

Categorical Principal Component Analysis and Application by Depression Data Set

“…Bu bağlamda, Kategorik temel bileşenler analizi ön plana çıkmaktadır. 10 Kategorik temel bileşenler analizi, geometrik olarak ele alındığında, gerçek uzay yerine daha düşük boyutlu bir uzayda değişken ve kategorileri arasındaki ilişkileri grafiksel olarak göstermeyi amaçlamaktadır. Diğer bir ifade ile yöntem, sayısal dönüşümler yaparak, X'in nesne skorlarını bulmayı ve Yj'nin bir dizisini çeşitli yollarla kısıtlayarak minimize etmeyi amaçlamaktadır.…”