TY  - JOUR
TI  - Introduction of Nonlinear Principal Component  Analysis with an Application in Health Science Data
AB  - Nonlinear Principal Component Analysis is one of the explanatory dimension reducing technique and presents numerical  and graphical results for variable set included linear or nonlinear relationships. In this study, Nonlinear Principal  Components Analysis was introduced and the relationship between students' sexual and physical trauma stories and  demographic characteristics was examined with this method. In the study, the relationship between trauma and 9 variables  obtained by questionnaire from 548 students was evaluated by non-linear principal components analysis. The total  eigenvalue of first dimension has been found to be 1.766 and the total eigenvalue of second dimension ha s been found to  be 1.504 The variance explanation rate of these eigenvalues are 17.656% and 15.044% respectfully. The total explained  variance is seen as 28.550%. With nonlinear principal component analysis, categorical variables are scaled to the desired  size in the most appropriate way, and thus, nonlinear relationships can be modeled as well as linear relationships between  variables. With this analysis, gender, age, marital status and suicide variables were found to be effective on trauma.
AU  - DEMİR, Canan
AU  - KESKİN, SIDDIK
DO  - 10.5505/ejm.2022.09068
PY  - 2022
JO  - Eastern Journal of Medicine
VL  - 27
IS  - 3
SN  - 1301-0883
SP  - 309
EP  - 402
DB  - TRDizin
UR  - http://search/yayin/detay/1123883
ER  -