eLife

TopoMetry systematically learns and evaluates the latent geometry of single-cell data

A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...
IEEE

Unsupervised Feature Dimensionality Reduction via Latent Low-Rank Embedding Projection for Classification of Hyperspectral Images

Abstract: To deal with the curse of dimensionality in hyperspectral images, numerous feature dimensionality reduction (FDR) methods have been proposed to map high-dimensional data into a ...
IEEE

Privacy-Preserving, Efficient, and Accurate Dimensionality Reduction

Abstract: The swift evolution of artificial intelligence and big data has dramatically increased data volume and computational complexity, thereby considerably escalating data storage and processing ...
GitHub

A deterministic, linear-time alternative to t-SNE for dimensionality reduction.

Landmark Triangulation is a dimensionality reduction algorithm designed for speed, stability, and massive scalability. Unlike t-SNE or UMAP, which rely on iterative optimization and O(N²) pairwise ...