Reproducing Kernel Hilbert Spaces #
This file defines vector-valued reproducing Kernel Hilbert spaces, which are Hilbert spaces of functions, as well as characterizing these spaces in terms of infinite-dimensional positive semidefinite matrices.
Main results #
RKHS: the class of reproducing kernel Hilbert spacesRKHS.kernel: the kernel of a RKHS as a matrix.RKHS.kerFun: the kernel functions of a RKHS.RKHS.kerFun_dense: the kernel functions are dense in the Hilbert space.RKHS.posSemidef_kernel: The kernel is positive semidefinite.RKHS.OfKernel: RKHS constructed from a positive semidefinite matrix.RKHS.kernel_ofKernel: The kernel of the constructed RKHS is equal to the matrix, this is essentially Moore's theorem.
TODO #
- Privatize
RKHS.OfKernel
References #
A reproducing kernel Hilbert space is a Hilbert space with an injection to functions mapping into another Hilbert space, such that point evaluation is continuous.
Continuous injection to functions from the reproducing kernel Hilbert space
Hto functions from the domainXto the Hilbert spaceV- coeCLM_injective : Function.Injective ⇑(coeCLM 𝕜)
Instances
Each element of a reproducing kernel Hilbert space may be coerced into a function.
Equations
- RKHS.instFunLike = { coe := fun (f : H) => (RKHS.coeCLM 𝕜) f, coe_injective' := ⋯ }
The kernel functions of a reproducing kernel Hilbert space are the adjoint of the point evaluation.
Equations
Instances For
The kernel of a reproducing kernel Hilbert space is a matrix of entries given by the kernel functions.
Equations
- RKHS.kernel H = Matrix.of fun (x y : X) => (ContinuousLinearMap.adjoint (RKHS.kerFun H x)).comp (RKHS.kerFun H y)
Instances For
The "reproducing" property of the kernel functions, left version.
The "reproducing" property of the kernel functions, right version.
The "reproducing" property of the kernel.
The span of the kernel functions is dense.
The kernel is a positive semidefinite matrix.
Construction of RKHS from kernel #
Equations
- One or more equations did not get rendered due to their size.
The reproducing kernel Hilbert space generated by a positive semidefinite matrix. TODO: Privatize.
Equations
Instances For
Equations
- RKHS.OfKernel.instRKHS = { coeCLM := ContinuousLinearMap.pi fun (x : X) => ContinuousLinearMap.adjoint (RKHS.OfKernel.kerFun✝ K x), coeCLM_injective := ⋯ }
The kernel of the reproducing kernel Hilbert space generated by a positive semidefinite matrix is the original positive semidefinite matrix.