Isotropic position

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In the fields of machine learning, the theory of computation, and random matrix theory, a probability distribution over vectors is said to be in isotropic position if its covariance matrix is equal to the identity matrix.

Formal definitions[edit]

Let be a distribution over vectors in the vector space . Then is in isotropic position if, for vector sampled from the distribution,

A set of vectors is said to be in isotropic position if the uniform distribution over that set is in isotropic position. In particular, every orthonormal set of vectors is isotropic.

As a related definition, a convex body in is called isotropic if it has volume , center of mass at the origin, and there is a constant such that

for all vectors in ; here stands for the standard Euclidean norm.


See also[edit]

References[edit]

  • Rudelson, M. (1999). "Random Vectors in the Isotropic Position". Journal of Functional Analysis. 164 (1): 60–72. arXiv:math/9608208.