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Positive definiteness is exactly the inverse condition for the observed GP covariance
Statement
In the finite observation model, let and let be symmetric and positive semidefinite. Then is symmetric and positive semidefinite. Its ordinary inverse exists if and only if is positive definite, equivalently if and only if all singular values of are positive. Thus the standard inverse formulas are defined exactly under ; no ordinary inverse formula applies when is singular.
Statement references
Facts & Assumptions
Given: A finite observation matrix from a symmetric positive-semidefinite kernel and a symmetric positive-semidefinite noise covariance .
Every finite Gram matrix of the kernel is positive semidefinite, and the observed matrix is . Finite Gaussian-process regression observation model
A finite-dimensional nonnegative operator has an orthonormal eigenbasis with nonnegative eigenvalues. A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S
The singular values of an endomorphism are the eigenvalues of . The singular values of a linear map as the eigenvalues of the positive square root of T^*T
A nonnegative finite-dimensional operator has a unique nonnegative square root. A non-negative operator has a unique non-negative square root
A finite-dimensional linear map has a singular-value decomposition with exactly its positive singular values in the nonzero diagonal block. Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
Proof
Given: The matrices and in the Statement.
For every , by [F1], and is symmetric. Hence is a nonnegative endomorphism of the finite-dimensional real inner-product space .
By [F2], choose an orthonormal eigenbasis of with eigenvalues . In this basis, is invertible exactly when no is zero, while for is positive for every nonzero exactly when all . Thus is invertible if and only if it is positive definite.
In the same basis, has eigenvalues . By [F4], its unique nonnegative square root has eigenvalues , so [F3] says the singular values of are precisely these . By [F5], the SVD has full rank exactly when all singular values are positive, which is exactly the invertibility condition in step 2.1.
Depends on
- Finite Gaussian-process regression observation model
- The singular values of a linear map as the eigenvalues of the positive square root of T^*T
- A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S
- A non-negative operator has a unique non-negative square root
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
Used by
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Sources
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, Chapter 4 §4.1 (standard reference, not scraped)
- Christopher M. Bishop, Pattern Recognition and Machine Learning, §6.4.2 (standard reference, not scraped)