The synergistic effect of QR decomposition with t-SNE

Abstract

The study utilized non-parametric tests, specifically, the Mann-Whitney U test, to evaluate the performance of a proposed model called QRPCA-t-SNE, along with two other models, MDS and UMAP. The study compared these three models with two datasets on performance metrics such as model accuracy, training accuracy, testing accuracy, mean square error, AUC scores, precision, recall, and F1 scores. Once the model's performance was conducted, the Anderson-Darling test was to check for data normality before applying the hypothesis for model proof. The analysis revealed that Model 1 (QRPCA-t-SNE) significantly outperformed Model 2 (UMAP) and Model 3 (MDS) in terms of accuracy, with p-values of 0.0027 and 0.0003, respectively. This finding suggests that Model 1 (QRPCA-t-SNE) is suitable for high-accuracy and reliability applications, providing valuable insights into predictive analytics with a 95% confidence interval (confidence level α= 0.05).