Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids

arXiv:2403.20246v1 Announce Type: new
Abstract: Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of the x and y axes due to the complexities inherent in dimension reduction. This study addresses this challenge by using the x and y coordinates derived from …

abstract arxiv biomedical challenge class cs.hc cs.lg data datasets dimensions feature interpretability lies observation plot plots type