högredimensional
högredimensional, or high-dimensional, is a term used in mathematics, statistics, and data science to describe spaces or datasets that have a large number of dimensions or features. Unlike the familiar three spatial dimensions, high-dimensional contexts involve many coordinates per observation, often with more features than observations.
This high dimensionality introduces challenges collectively referred to as the curse of dimensionality. As dimensionality grows,
To cope, researchers use dimensionality reduction and feature selection. Techniques such as principal component analysis (PCA),
High-dimensional data are common in genomics, image and text analysis, finance, and sensor networks, where the
Important theoretical ideas include the Johnson-Lindenstrauss lemma, which asserts that a set of points can be
High-dimensional analysis is distinct from infinite-dimensional contexts, such as function spaces in analysis. The distinction matters
Example: a gene-expression dataset with thousands of genes and a few dozen samples typifies high-dimensional data