In spatial statistics, Gaussian processes, kernel machines and covariance estimation we are concerned with covariances between different the values of stochastic processes at various values of their indices.

Covariance kernels apply to much more general creatures than just Gaussian processes; any process with finite second moments has a covariance function. They are especially useful for Gaussian process methods, since, basically, Gaussian processes are uniquely specified by their mean and covariance kernels, and also have convenient algebraic properties by virtue of being Gaussian, and there are some famous special cases where you can do elegant things with them in this context. More generally, they arise in pretty much anything where representer theorems hold.

What follows are some useful kernels to have in my toolkit, mostly over \(\mathbb{R}^n\) or at least some space with a metric. There are many more than I could fit here, over many more spaces than I need. (Real vectors, strings, other kernels, probability distributions etc.)

For these I have freely raided David Duvenaudâs crib notes which became a thesis chapter (Duvenaud 2014). Also wikipedia and (Abrahamsen 1997; Genton 2002).

## General real covariance kernels

A function \(K:\mathcal{T}\times\mathcal{T}\to\mathbb{R}\) can be a covariance kernel if

- It is symmetric in its arguments \(K(s,t)=K(t,s)\) (more generally, conjugate symmetric â \(K(s,t)=K^*(t,s)\), but I think maybe my life will be simpler if I ignore the complex case for the moment.)
- It is positive semidefinite.

That positive semidefiniteness means that for arbitrary real numbers \(c_1,\dots,c_k\) and arbitrary indices \(t_1,\dots,t_k\)

\[ \sum_{i=1}^{k} \sum_{j=1}^{k} c_{i} c_{j} K(t_{i}, t_{j}) \geq 0 \]

The interpretation here is that this is the same as the covariance of different random variables induced by the process, and we need them to be consistent, which implies that it is necessary that

\[ \operatorname{Var}\left\{c_{1} X_{\mathbf{t}_{1}}+\cdots+c_{k} X_{\mathbf{t}_{k}}\right\}= \sum_{i=1}^{k} \sum_{j=1}^{k} c_{i} c_{j} K\left(\mathbf{t}_{i}, \mathbf{t}_{j}\right) \geq 0 \]

This arises from the constraint on the covariance of \(\operatorname{Var}(\mathbf X\in \mathbb{R}_+^d\) which requires that for \(\mathbf {b}\in \mathbb{R}^d\)

\[ \operatorname {var} (\mathbf {b} ^{\top}\mathbf {X} ) =\mathbf {b} ^{\top}\operatorname {var} (\mathbf {X} )\mathbf {b} \]

What can we say about this covariance if every element of \(\mathbf X\) is non-negative?

Amazingly (to me), this necessary condition will also be sufficient to make something a covariance kernel. In practice designing covariance functions using positive definiteness is tricky; the space of positive definite kernels is implicit. What we normally do is find a fun class that guarantees positive definiteness and riffle through that. Most of the rest of this notebook is devoted to such classes.

## Bonus: complex covariance kernels

I talked in terms of real kernels above because I generally observer real measurements of processes. But often complex covariances arise in a natural way too.

A function \(K:\mathcal{T}\times\mathcal{T}\to\mathbb{C}\) can be a covariance kernel if

- It is symmetric
*conjugate*symmetric in its arguments â \(K(s,t)=K^*(t,s)\), - It is positive semidefinite.

That positive semidefiniteness means that for arbitrary complex numbers \(c_1,\dots,c_k\) and arbitrary indices \(t_1,\dots,t_k\)

\[ \sum_{i=1}^{k} \sum_{j=1}^{k} c_{i} \overline{c_{j}} K(t_{i}, t_{j}) \geq 0. \]

Every analytic real kernel should also be a complex kernel, right? đ§.

## Stationary

A popular assumption, more or less implying implies that no region of the process is *special*. In this case the kernel may be written as a function purely of the distance between observations, i.e.

\[K(s,t)=K(\|s,t|)\] for some distance \(\|\cdot\|\) between the observation coordinates.

## Wiener process kernel

From the naming, we might suspect that a Gaussian process would also describe a standard Wiener process, which after all is a process with Gaussian *increments*, which is certain type of dependence. It is over a boring index space, time \(t\in \mathbb{R}\), but there is indeed nothing stopping us.

We can read this right off the Wiener process Wikipedia page. For a Gaussian process \(\{W_t\}_{t\in\mathbb{R}},\)

\[ {\displaystyle \operatorname {cov} (W_{s},W_{t})=s \wedge t} \]

Here \(s \wedge t\) here means âthe minimum of \(s\) and \(t\)â.

That result is standard. From it we can immediately construct the kernel \(K(s,t):=s \wedge t\).

## Causal kernels

Time-indexed processes more general than a standard Wiener process. We can construct kernels that are more general than this, right? đ§

TODO: pd in this context? stationary in this context? homogenous in this context?

## Markov kernels

How can we know from inspecting a kernel whether it implies an independence structure of some kind? The Wiener process and causal kernels clearly imply certain independences. Any kernel \(k(s,t)=k(s\wedge t)\) is clearly Markov. Are there more general ones? TODO: relate to kernels of bounded support. đ§

TODO: pd in this context? stationary in this context? homogenous in this context?

## Squared exponential

The classic, default, analytically convenient, because it is proportional to the Gaussian density and therefore cancels out with it at opportune times.

\[k_{\textrm{SE}}(x, x') = \sigma^2\exp\left(-\frac{(x - x')^2}{2\ell^2}\right)\]

## Rational Quadratic

Duvenaud reckons this is everywhere but TBH I have not seen it. Included for completeness.

\[k_{\textrm{RQ}}(x, x') = \sigma^2 \left( 1 + \frac{(x - x')^2}{2 \alpha \ell^2} \right)^{-\alpha}\]

Note that \(\lim_{\alpha\to\infty} k_{\textrm{RQ}}= k_{\textrm{SE}}\).

## MatĂ©rn

The MatĂ©rn stationary (and in the Euclidean case, isotropic) covariance function is one model for covariance. See Carl Edward Rasmussenâs Gaussian Process lecture notes for a readable explanation, or chapter 4 of his textbook (Rasmussen and Williams 2006).

\[ k_{\textrm{Mat}}(x, x')=\sigma^{2} \frac{2^{1-\nu}}{\Gamma(\nu)}\left(\sqrt{2 \nu} \frac{x - x'}{\rho}\right)^{\nu} K_{\nu}\left(\sqrt{2 \nu} \frac{x - x'}{\rho}\right) \]

where \(\Gamma\) is the gamma function, \(\ K_{\nu }\) is the modified Bessel function of the second kind, and \(\rho,\nu\geq 0\).

AFAICT you use this for covariances hypothesised to be less smooth than the squared exponential covariance. And other things?

## Periodic

\[ k_{\textrm{Per}}(x, x') = \sigma^2\exp\left(-\frac{2\sin^2(\pi|x - x'|/p)}{\ell^2}\right) \]

## Locally periodic

This is an example of a composed kernel, explained below.

\[\begin{aligned} k_{\textrm{LocPer}}(x, x') &= k_{\textrm{Per}}(x, x')k_{\textrm{SE}}(x, x') \\ &= \sigma^2\exp\left(-\frac{2\sin^2(\pi|x - x'|/p)}{\ell^2}\right) \exp\left(-\frac{(x - x')^2}{2\ell^2}\right) \end{aligned}\]

Obviously there are other possible localisations of a periodic kernel. This is *a* Locally Periodic kernel.

## With atoms

đ§ Is this feasible? What are the constructions that allow discontinuity in the process and what difficulties to they engender?

## âIntegralâ kernel

I just noticed the ambiguously named Integral kernel:

Iâve called the kernel the âintegral kernelâ as we use it when we know observations of the integrals of a function, and want to estimate the function itself.

Examples include:

- Knowing how far a robot has travelled after 2, 4, 6 and 8 seconds, but wanting an estimate of its speed after 5 secondsâŠ
- Wanting to know an estimate of the density of people aged 23, when we only have the total count for binned age rangesâŠ

I would argue that *all* kernels are naturally defined in terms of integrals, but the author seems to mean something particular. I suspect I would call this a sampling kernel, but that name is also overloaded. Anyway, what is *actually* going on here? Where is it introduced? Possibly one of (Smith, Alvarez, and Lawrence 2018; OâCallaghan and Ramos 2011; **???**).

## Composing kernels

A sum or product (or outer sum, or tensor product) of kernels is still a kernel. For other transforms YMMV.

For example, in the case of Gaussian processes, suppose that, independently,

\[\begin{aligned} f_{1} &\sim \mathcal{GP}\left(\mu_{1}, k_{1}\right)\\ f_{2} &\sim \mathcal{GP}\left(\mu_{2}, k_{2}\right) \end{aligned}\] then

\[ f_{1}+f_{2} \sim \mathcal{GP} \left(\mu_{1}+\mu_{2}, k_{1}+k_{2}\right) \] so \(k_{1}+k_{2}\) is also a kernel.

More generally, if \(k_{1}\) and \(k_{2}\) are two kernels, and \(c_{1}\), and \(c_{2}\) are two positive real numbers, then:

\[ K(x, x')=c_{1} k_{1}(x, x')+c_{2} k_{2}(x, x') \] is again a kernel. What with the multiplication as well, we note that all polynomials of kernels where the coefficients are positive are in turn kernels. (Genton 2002)

Note that the additivity in terms of kernels is not the same as additivity in terms of induced feature spaces. The induced feature map of \(k_{1}+k_{2}\) is their *concatenation* rather than their sum. Suppose \(\phi_{1}(x)\) gives us the feature map of \(k_{1}\) for \(x\) and likewise \(\phi_{2}(x)\).

\[\begin{aligned} k_{1}(x, x') &=\phi_{1}(x)^{\top} \phi_{1}(x') \\ k_{2}(x, x') &=\phi_{2}(x)^{\top} \phi_{2}(x')\\ k_{1}(x, x')+k_{2}(x, x') &=\phi_{1}(x)^{\top} \phi_{1}(x')+\phi_{2}(x)^{\top} \phi_{2}(x')\\ &=\left[\begin{array}{c}{\phi_{1}(x)} \\ {\phi_{2}(x)}\end{array}\right]^{\top} \left[\begin{array}{c}{\phi_{1}(x')} \\ {\phi_{2}(x')}\end{array}\right] \end{aligned}\]

If \(k_{y}:\mathcal{Y}\times\mathcal{Y}\to\mathbb{R}\) is a kernel and \(\psi: \mathcal{X}\to\mathcal{Y}\) this is also a kernel

\[\begin{aligned} k_{x}:&\mathcal{X}\times\mathcal{X}\to\mathbb{R}\\ & (x,x')\mapsto k_{y}(\psi(x), \psi(x')) \end{aligned}\]

which apparently is now called a *deep kernel*.

Also if \(A\) is a positive definite operator, then of course it defines a kernel \(k_A(x,x'):=x^{\top}Ax'\)

(Genton 2002) uses the properties of covariance to construct some other nifty ones:

Let \(h:\mathcal{X}\to\mathbb{R}^{+}\) have minimum at 0. Then, using the identity for RVs

\[ \mathop{\textrm{Cov}}\left(Y_{1}, Y_{2}\right)=\left[\mathop{\textrm{Var}}\left(Y_{1}+Y_{2}\right)-\mathop{\textrm{Var}}\left(Y_{1}-Y_{2}\right)\right] / 4 \]

we find that the following is a kernel

\[ K(x, x')=\frac{1}{4}[h(x+x')-h(x-x')] \]

All these to various cunning combination strategies, which I will likely return to discuss. đ§ Some of them are in the references. For example (Duvenaud et al. 2013) position their work in the wider field.

There is a large body of work attempting to construct a rich kernel through a weighted sum of base kernels (e.g.Â (Bach 2008; Christoudias, Urtasun, and Darrell 2009)). While these approaches find the optimal solution in polynomial time, speed comes at a cost: the component kernels, as well as their hyperparameters, must be specified in advance[âŠ]

(Hinton and Salakhutdinov 2008) use a deep neural network to learn an embedding; this is a flexible approach to kernel learning but relies upon finding structure in the input density, p(x). Instead we focus on domains where most of the interesting structure is in f(x).

(Wilson and Adams 2013) derive kernels of the form \(SE Ă \cos(x â x_0\)), forming a basis for stationary kernels. These kernels share similarities with \(SE Ă Per\) but can express negative prior correlation, and could usefully be included in our grammar.

See (Grosse et al. 2012) for a mind-melting compositional matrix factorization diagram, constructing a search over hierarchical kernel decompositions.

Examples of existing machine learning models which fall under our framework. Arrows represent models reachable using a single production rule. Only a small fraction of the 2496 models reachable within 3 steps are shown, and not all possible arrows are shown.

## Stationary spectral kernels

(Sun et al. 2018; Bochner 1959; Kom Samo and Roberts 2015; Yaglom 1987) construct spectral kernels in the sense that they use the spectral representation to design the kernel and guarantee it is positive definite and stationary using Bochnerâs theorem

Bochnerâs theorem: A complex-valued function \(K\) on \(\mathbb{R}^{d}\) is the covariance function of a weakly stationary mean square continuous complex-valued random process on \(\mathbb{R}^{d}\) if and only if it can be represented as

\[ K(\boldsymbol{\tau})=\int_{\mathbb{R}^{P}} \exp \left(2 \pi i \boldsymbol{w}^{\top} \boldsymbol{\tau}\right) \psi(\mathrm{d} \boldsymbol{w}) \] where \(\psi\) is a positive and finite measure. If \(\psi\) has a density \(S(\boldsymbol{w})\), then \(S\) is called the spectral density or power spectrum of \(K\), i.e.Â \(S\) and \(K\) are Fourier duals.

## Non-stationary spectral kernels

(Sun et al. 2018; Remes, Heinonen, and Kaski 2017; Kom Samo and Roberts 2015) use a generalised Bochner Theorem (Yaglom 1987) which does not presume anything about stationarity:

A complex-valued bounded continuous function \(K\) on \(\mathbb{R}^{d}\) is the covariance function of a mean square continuous complex-valued random process on \(\mathbb{R}^{d}\) if and only if it can be represented as

\[ K(\boldsymbol{s}, \boldsymbol{t})=\int_{\mathbb{R}^{d} \times \mathbb{R}^{d}} e^{2 \pi i\left(\boldsymbol{w}_{1}^{\top} \boldsymbol{s}-\boldsymbol{w}_{2}^{\top} \boldsymbol{t}\right)} \psi\left(\mathrm{d} \boldsymbol{w}_{\mathbf{1}}, \mathrm{d} \boldsymbol{w}_{\mathbf{2}}\right) \]

This is clearly more general, but it is not immediately clear how to use this extra potential; spectral representations are not an intuitive way of constructing things.

## Locally stationary

(Genton 2002) defines these are kernels that have a particular structure, specifically, a kernel that can be factored into a stationary kernel (_2) and a non negative function \(K_1\) in the following way:

\[ K(\mathbf{s}, \mathbf{t})=K_{1}\left(\frac{\mathbf{s}+\mathbf{t}}{2}\right) K_{2}(\mathbf{s}-\mathbf{t}) \]

Global structure then depends on the mean location \(\frac{\mathbf{s}+\mathbf{t}}{2}\). (Genton 2002) describes some nifty spectral properties of these kernels.

Other constructions might vie for the title of âlocally stationaryâ. To check. đ§

## Genton kernels

Thatâs my name for them because they seem to originate in (Genton 2002).

For any non-negative function \(h:\mathcal{T}\to\mathbb{R}^+\) with \(h(\mathbf{0})=0,\) the following is a kernel:

\[ K(\mathbf{s}, \mathbf{t})=\frac{1}{4}[h(\mathbf{s}+\mathbf{t})-h(\mathbf{s}-\mathbf{t}) \] Genton gives the example of \(h:s,t\mapsto s^\top t.\)

The motivation is the identity

\[ \operatorname { Covariance }\left(Y_{1}, Y_{2}\right)= \left[\operatorname { Variance }\left(Y_{1}+Y_{2}\right)-\operatorname { Variance }\left(Y_{1}-Y_{2}\right)\right] / 4. \]

## Compactly supported

We usually think about these n the stationary isotropic case, c where we mean kernels that vanish whenever the distance between two observation \(s,t\) is larger than a certain cut-off distance \(L,\) i.e.Â \(\|s-t\|>L\Rightarrow K(s,t)=0\). These are great because they make the Gram matrix sparse (for example, if the cut-off is much smaller than the diameter of the observations and most observations have few covariance neighbours) and so can lead to computational efficiency even for exact inference without any special tricks. They donât seem to be popular? Statisticians are generally nervous around inferring the support of a parameter, or assigning zero weight to any region of a prior without very good reason, and I imagine this carries over to the analogous problem of covariance kernel support?

Despite feeling that qualm intuitively myself I think there are good cases for this kind of kernel; for example, in a hierarchical kernels you can get correlation between kernels of disjoint support.

(Genton 2002) mentions

\[ \max \left\{\left(1-\frac{\|\mathbf{s}-\mathbf{t}\|}{\tilde{\theta}}\right)^{\tilde{\nu}}, 0\right\} \] and handballs us to (Gneiting 2002) for a bigger smĂ¶rgĂ„sbord of stationary compactly supported kernels. Gneiting has a couple of methods designed to produce certain smoothness properties at boundary and origin, but mostly about producing compactly supported kernels via clever integral transforms.

## Kernels with desired symmetry

(Duvenaud 2014, chap. 2) summarises Ginsbourger et alâs work on kernels with desired symmetries / invariances. đ§ This produces for example, the periodic kernel above, but also such cute tricks as priors over MĂ¶bius strips.

## Learning kernels

This is usually in the context of Gaussian processes where everything can work out nicely if you are lucky. The goal for all these seems to be to maximise the marginal posterior likelihood, a.k.a. model evidence, which will be familiar from every Bayesian ML method ever.

### Learning kernel hyperparameters

đ§

### Learning kernel composition

Automating kernel design by some composition of simpler atomic kernels. AFAICT this started from summaries like (Genton 2002) and went via Duvenaudâs aforementioned notes to became a small industry (Lloyd et al. 2014; Duvenaud, Nickisch, and Rasmussen 2011; Duvenaud et al. 2013; Grosse et al. 2012). A prominent example was the Automated statistician project by David Duvenaud, James Robert Lloyd, Roger Grosse and colleagues, which works by greedy combinatorial search over possible compositions.

More fashionable, presumably, are the differentiable search methods. For example, the AutoGP system (Krauth et al. 2016; Bonilla, Krauth, and Dezfouli 2016) incorporates tricks like these to use gradient descent to design kernels for Gaussian processes. (Sun et al. 2018) construct deep networks of composed kernels. I imagine the Deep Gaussian Process literature is also of this kind, but have not read it.

### Hyperkernels

Kernels on kernels, for learning kernels. đ§ (Ong, Smola, and Williamson 2005, 2002; Ong and Smola 2003; Kondor and Jebara 2006)

## Non-positive kernels

As in, kernels which are not positive-definite. (Ong et al. 2004) đ§

## Covariance kernels of non-negative fields

Examining the positive-definiteness of the covariance of \(\operatorname{Var}(\mathbf X\in \mathbb{R}_+^d\) which requires that for \(\mathbf {b}\in \mathbb{R}^d\)

\[ \operatorname {var} (\mathbf {b} ^{\top}\mathbf {X} )=\mathbf {b} ^{\top}\operatorname {var} (\mathbf {X} )\mathbf {b} \]

what can we say about this covariance if every element of \(\mathbf X\) is non-negative? Clearly this corresponds to the assumption that the field is not Gaussian. đ§

See also non-negative matric factorization.

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