Intrinsic Coordinate

Intrinsic coordinates represent data points within a lower-dimensional space that captures the underlying structure of the original high-dimensional data, aiming to reduce noise and improve efficiency in various applications. Current research focuses on robustly computing these coordinates using techniques like manifold learning, functional data analysis, and topological data analysis, often incorporating algorithms such as generalized Procrustes analysis and principal component analysis. This approach finds applications in diverse fields, including genomics, brain imaging, image recognition, and neural network optimization, by enabling more efficient data representation, improved visualization, and faster model training.