A hybrid model for data visualization using linear algebra methods and machine learning algorithm

Abstract

The t-distributed stochastic neighbor embedding (t-SNE) is a powerful technique for visualizing high-dimensional datasets. By reducing the dimensionality of the data, t-SNE transforms it into a format that can be more easily understood and analyzed. The existing approach is to visualize high-dimensional data but not deeply visualize. This paper proposes a model that enhances visualization and improves the accuracy. The proposed model combines the non-linear embedding technique t-SNE, the linear dimensionality reduction method principal component analysis (PCA), and the QR decomposition algorithm for discovering eigenvalues and eigenvectors. In Addition, we quantitatively compare the proposed model QRPCA-t-SNE with PCA-t-SNE using the following criteria: data visualization with different perplexity and different principal components, confusion matrix, model score, mean square error (MSE), training, testing accuracy, receiver operating characteristic curve (ROC) score, and AUC score.

The synergistic effect of QR decomposition with t-SNE

“…This paper presents a comparative analysis of the dimensionality reduction technique and proposed model QRPCA-t-SNE [24]. The first method we considered is QRPCA-t-SNE [24], which combines QR decomposition and principal component analysis with t-distributed stochastic neighbor edging. The second method is UMAP, which stands for uniform manifold approximation and projection.…”

“…Figure 10. Mean square error of UMAP [24] Figure 11. Training and testing accuracy of UMAP [24] Figure 12.…”

“…Mean square error of UMAP [24] Figure 11. Training and testing accuracy of UMAP [24] Figure 12. AUC score of UMAP [24] Figure 13.…”

“…The QRPCA-t-SNE [24] is a deep learning model proposed to help visualize high-dimensional datasets, particularly in genomics. The model is built using a combination of machine learning algorithms and algebraic methods, such as the QR decomposition algorithm [25], principal component analysis, and t-SNE.…”

“…This paper compares different dimensionality reduction techniques, such as UMAP and MDS, with the Proposed model (QRPCA-t-SNE) [24] based on various parameters. We aim to demonstrate that the Proposed model is more robust than other dimensionality reduction techniques.…”