Multidimensional Scaling

Multidimensional scaling (MDS) is a family of dimensionality reduction techniques aiming to represent high-dimensional data in a lower-dimensional space while preserving pairwise distances or similarities. Current research focuses on improving the scalability of MDS algorithms for large datasets, often employing out-of-sample extensions or stochastic approaches to reduce computational complexity, and exploring variations like non-metric MDS and hyperbolic MDS to handle different data structures and uncertainties. These advancements enhance the applicability of MDS in diverse fields, including data visualization, machine learning, and subsurface analysis, by enabling efficient processing of large datasets and providing more robust and interpretable low-dimensional representations.