This is a question that I can't find the solution. I don't know it is a open question or it is a well-known result that can be attained from several lemmas.

Here are the definition of Matérn class kernel:


where $\sigma^2$ is a length scale parameter, $\Gamma(.)$ is gamma function, $K_{\nu}$ is the modified Bessel function of the second kind, and $\nu,\rho$ are non-negative parameters of covariance.

I use the definition of universal kernel in Universal Kernels(Charles A. Micchelli):

Given any prescribed compact subset Z of a Hausdorff topological space X, A kernel is universal if for any positive number $\epsilon$ and any function f ∈ C(Z) (continuous function on Z) there is a function g ∈ K(Z) (RKHS space generated by the kernel) such that sup norm $||f-g||_{Z}<\epsilon$

I know that frequently used Gaussian and Laplacian kernels are universal kernels and they are special case of Matérn class so I am wondering what kind of $\nu$ will lead to a universal kernel.

I am interesting in this problem because I am now studying Gaussian Process and Bayesian Optimization [Niranjan Srinivas,Jonathan Scarlett, Jasper Snoek] and I find that not only in theory they consider Matérn/Gaussian Kernel but also in real-world implementation. The default Kernel choice of many Bayesian optimization tools are Matérn 5/2. I don't think it is reasonable if it's not universal but I don't know how to prove it.


1 Answer 1


Yes, Matérn kernels are universal, for any parameters.

First note that $M_{\sigma^2,\nu,\rho}(x, y) = \Psi(x - y)$, i.e. the kernel is translation-invariant. It is also bounded and continuous.

The first main result we'll need is from:

Sriperumbudur, Fukumizu, and Lanckiret. Universality, Characteristic Kernels and RKHS Embedding of Measures. JMLR 12(Jul):2389−2410, 2011.

They show that the notion they call $cc$-universal is equivalent to the universality of Michhelli et al. (Remark 3). They also show that for bounded, continuous, translation-invariant kernels on $\mathbb R^d$, being characteristic implies being $cc$-universal (Figure 1 part 3).

It was previously shown that bounded continuous translation-invariant kernels on $\mathbb R^d$ are characteristic if and only if the Fourier transform of $\Psi$, which we know to be a nonnegative measure from Bochner's theorem, has support on all of $\mathbb R^d$: Theorem 7 of

Sriperumbudur, Gretton, Fukumizu, Lanckriet, and Schölkopf. Injective Hilbert Space Embeddings of Probability Measures. 21st Annual Conference on Learning Theory, 2008.

But the Fourier transform of $\Psi$ for the Matérn kernel, from equation (4.15) of Rasmussen and Williams, Gaussian Processes in Machine Learning (chapter 4 pdf), is the density $$ S(s) = \sigma^2 \frac{2^d \pi^{d/2} \Gamma(\nu + d/2) (2 \nu)^\nu}{\Gamma(\nu) \rho^{2 \nu}} \left( \frac{2 \nu}{\rho^2} + 4 \pi^2 \lVert s \rVert^2 \right)^{-\nu + d/2} $$ where $d$ is the dimension. This of course has support on all of $\mathbb R^d$, so the Matérn kernel is characterestic, so it's $cc$-universal.

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    $\begingroup$ Thank you so much for your self-contained and clear answer. To be honest, I do find Sriperumbudur's paper and find that Matern class is cc-universal. But I just read the introduction so I don't realize that they are equivalent. And I also have Gaussian Processes in Machine Learning at hand. So I guess the reason why we often use Matern 3/2 and Matern 5/2 is because that they have simple form and is 1/2-times differentiable. $\endgroup$ Jun 28, 2018 at 17:34
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    $\begingroup$ I think that I need to study more math. If I want to understand the story between Gaussian Process and RKHS space, what reference would you recommend me to study? $\endgroup$ Jun 28, 2018 at 17:37
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    $\begingroup$ GPML has a decent basic overview. I don't know of a good more-detail-than-that-but-not-mega-technical source; it's a significant gap in kernel methods right now imo.... $\endgroup$
    – Danica
    Jun 28, 2018 at 19:55
  • $\begingroup$ Could you further explain the last sentence, what is the current interest in kernel method right now? I am a CS master student. I am a layman of kernel method, I study it because I try to understand Bayesian Optimization and since they often use GP as a prior, kernel is everywhere. I only treat kernel as a tool to control smoothness (Kridging and kernel correspond to mean square differentibility) it will be good to know what kernel method specialist actually cares about right now. $\endgroup$ Jun 28, 2018 at 20:53
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    $\begingroup$ Sorry, I didn't mean that it's a research gap, just that I'm not aware of a book that is detailed but not too hardcore mathy. Berlinet-Thomas/Agnan and Steinwart/Christmann are excellent detailed mathematical books about the topic. $\endgroup$
    – Danica
    Jun 28, 2018 at 20:56

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