Improving Dimensionality Reduction Projections for Data Visualization

In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These involve the transformation of high-dimensional into reduced versions, typically in 2D, with aim preserving significant properties from original data. Many algorithms exist, nonlinear approaches such as t-SNE (t-Distributed Stochastic Neighbor Embedding) UMAP (Uniform Manifold Approximation Projection) gained popularity field information visualization. this paper, we introduce a simple yet powerful manipulation vector datasets that modifies their values based on weight frequencies. This technique significantly improves results across various scenarios. To demonstrate efficacy our methodology, conduct an analysis collection well-known labeled The improved clustering performance when attempting to classify space. Our proposal presents comprehensive adaptable approach enhance outcomes visual exploration.