DiRe - JAX: A JAX based Dimensionality Reduction Algorithm for Large-scale Data

DiRe -JAX is a new dimensionality reduction toolkit designed to address some of the challenges faced by traditional methods like UMAP and tSNE, such as loss of global structure and computational efficiency.Built on the JAX framework, DiRe leverages modern hardware acceleration to provide an efficient, scalable, and interpretable solution for visualizing complex data structures and for quantitative analysis of lower-dimensional embeddings.The toolkit shows considerable promise in preserving both local and global structures within the data as compared to state-of-the-art UMAP and tSNE implementations.This makes it suitable for a wide range of applications in machine learning, bioinformatics, and data science. Statement of needTraditional dimensionality reduction techniques such as UMAP and tSNE are widely used for visualizing high-dimensional data in lower-dimensional spaces, usually 2D and sometimes 3D.Other uses include dimensionality reduction to other, possibly higher and thus non-visual dimensions, for the subsequent use of classifiers such as SVMs.