Let's say we use a kernel regularization algorithm such as ridge regression to minimize some loss in an RBF kernel:

$$\min_{h \in H} \frac{1}{n} \sum_i (h(x_i) - y(x_i))^2 + ||h||^2_K$$

We get some $h$ in the RBF kernel space. What now happens if we square h? For example, if we look at $h^2$, is it still in an RBF kernel space or not?

Due to the representer theorem we know that $h$ is a sum of kernel products. A guess is to say that $h(x) = e^{-||x - z||^2/\sigma^2}$, and if we look at $h^2$ we get that $h^2(x) = e^{-2||x - z||^2/\sigma^2}$. But if $h$ contains two terms: $h(x) = e^{-||x - z||^2/\sigma^2} + e^{-||x - y||^2/\sigma^2}$, and we look at $h^2(x)$ we find that:

$$h^2(x) = e^{-2||x - z||^2/\sigma^2} + e^{-2||x - y||^2/\sigma^2} - 2e^{(-||x-y||^2-||x-z||^2)/\sigma^2}$$

And I can't see now we have two terms, how to rewrite it to a sum of RBF kernel products. So does it stay in the same RBF space? And how about the norm of h?


1 Answer 1


To start with, you're slightly off on the representer theorem; it means that $h$ is a linear combination of kernel functions. That is, if your input data points are $\{z_i\}_{i=1}^n$, then $$ h(x) = \sum_{i=1}^n \alpha_i \, k(x, z_i) $$ for some real coefficients $\alpha_i$.

Thus $$ h^2(x) = \left( \sum_i \alpha_i \, k(x, z_i) \right)^2 = \sum_i \sum_j \alpha_i \,\alpha_j\, k(x, z_i) \, k(x, z_j) ,$$ which will be in a given RKHS as long as $k(x, z_i) \, k(x, z_j)$ is in the RKHS.

Now specialize to the class of RBF kernels $$ k_\sigma(x, z) = \exp\left( - \frac{1}{2 \sigma^2} \lVert x - z \rVert^2 \right) ,$$ and define the corresponding RKHS as $\mathcal H_\sigma$. (Note that your definition didn't have the 2 in the denominator, which is nonstandard and somewhat misleading; I'll use this one.)

Now, it turns out that: $$ \begin{align} k_\sigma(x, z_i) \, k_\sigma(x, z_j) &= k_{\sqrt{2}\sigma}(z_i, z_j) \, k_{\sigma/\sqrt{2}}\left(x, \frac{z_i + z_j}{2} \right) \end{align} .$$ Thus \begin{align} h^2(x) &= \sum_{ij} \underbrace{\alpha_i \alpha_j k_{\sqrt{2}\sigma}(z_i, z_j)}_{\beta_{ij}} \, k_{\sigma/\sqrt{2}}\Big(x, \underbrace{\frac{z_i + z_j}{2}}_{\zeta_{ij}} \Big) \\&= \sum_{ij} \beta_{ij} \, k_{\sigma/\sqrt{2}}(x, \zeta_{ij}) ,\end{align} and $h^2 \in \mathcal H_{\sigma/\sqrt{2}}$. In the interest of brevity, define $\tau := \sigma / \sqrt{2}$.

Now, turn to the following paper:

Tanaka, Imai, Kudo, and Miyakoshi, Theoretical analyses on a class of nested RKHS's, ICASSP 2011 (doi, author's pdf)

They show a few interesting facts in their Section 5:

First, $H_\sigma \subset H_\tau$ for all $\tau < \sigma$ (Theorem 5), and moreover $\lVert f \rVert_{\mathcal H_\sigma} \ge \lVert f \rVert_{\mathcal H_\tau}$ for every $f \in \mathcal H_{\sigma}$ (consequence of Theorem 4).

This doesn't tell us anything in particular about whether $h^2 \in H_\sigma$, but it does let us use their Proposition 1 as follows:

Let $\nu := \sqrt{ \sigma^2 + \sigma^2 - (\sigma / \sqrt{2})^2} = \sqrt{3/2} \sigma$. Then $$\langle k_{\sigma}(\cdot, x), k_{\sigma}(\cdot, y) \rangle_{\mathcal H_{\tau}} = k_\nu(x, y).$$

Thus \begin{align} \lVert h \rVert_{\mathcal H_\tau}^2 &= \lVert \sum_i \alpha_i k_\sigma(\cdot, z_i) \rVert_{\mathcal H_\tau}^2 \\&= \sum_{ij} \alpha_i \alpha_j \langle k_\sigma(\cdot, z_i), k_\sigma(\cdot, z_j)\rangle_{\mathcal H_\tau} \\&= \sum_{ij} \alpha_i \alpha_j k_\nu(z_i, z_j) \end{align} whereas \begin{align} \lVert h^2 \rVert_{\mathcal H_\tau}^2 &= \lVert \sum_{ij} \beta_{ij} k_\tau(\cdot, \zeta_{ij}) \rVert_{\mathcal H_\tau}^2 \\&= \sum_{abij} \beta_{ab} \beta_{ij} \langle k_\tau(\cdot, \zeta_{ab}), k_\tau(\cdot, \zeta_{ij})\rangle_{\mathcal H_\tau} \\&= \sum_{abij} \beta_{ab} \beta_{ij} k_\tau(\zeta_{ab}, \zeta_{ij}) \\&= \sum_{abij} \alpha_a \alpha_b \alpha_i \alpha_j k_{\sqrt{2}\sigma}(z_a, z_b) k_{\sqrt{2}\sigma}(z_i, z_j) k_\tau(\zeta_{ab}, \zeta_{ij}) .\end{align}

The diagonal terms ($a = i$, $b = j$) of the latter sum look kind of like the first sum, but the off-diagonals can be either positive or negative, so that doesn't lead to an immediate bound. At least in this form, though, the norms are numerically comparable.

  • $\begingroup$ Excellent answer @Dougal. However, I'm still quite interested in what happens to the norm of $||h||_K$ compared to the norm of $h^2$ in the new kernel $||h^2||_{K'}$, where $K'$ has bandwidth $\frac{\sigma}{\sqrt{2}}$. I wonder are these norms related somehow, or can we bound the norm of $h^2$ using the norm of $h$? However, the derivation gets quite tricky, since in $k(x,z_i)k(x,z_j)$ we get a factor that depends on $exp(||z_i - z_j||)$... which only neatly cancels if $z_i = z_j$. $\endgroup$
    – Tom
    Commented Feb 18, 2016 at 16:38
  • $\begingroup$ @Tom I updated with the equations for the norms, but it didn't really lead anywhere. Note though that norms in different spaces aren't necessarily comparable.... $\endgroup$
    – Danica
    Commented Feb 19, 2016 at 10:58
  • $\begingroup$ @Doagal thanks! I also worked it out and it doesn't seem comparable indeed. What confuses me a bit though is the following. Let's say you assume that we are working in a lineair kernel, and a function is bounded in the lineair kernel by some constant $\Theta$. Now if you square this function, it's easy to show it will end up in a polynomial kernel with degree 2 (and $c = 0$, see definition of link). Now the norm in this kernel is actually the same as in the lineair kernel. $\endgroup$
    – Tom
    Commented Mar 14, 2016 at 14:41
  • $\begingroup$ Since the RBF kernel has an implicit feature mapping, squaring a function in this space could be similar to squaring it in the euclidean kernel - I guess (?). Thus we should be able to construct a polynomial kernel using the feature mapping of the RBF kernel, and in this feature mapping the norm should be the same. However, this doesn't seem to hold, or the feature mapping of this space does not correspond to the RBF kernel with the different parameter $\sigma$... Any ideas? $\endgroup$
    – Tom
    Commented Mar 14, 2016 at 14:50
  • $\begingroup$ @Tom One of the feature mappings for the RBF kernel corresponds to basically the kernel itself, with every point on the space – see the second section of this answer. If you square that, you end up with the same type of argument as above, though the technicalities are trickier because it's infinite dimensional. I assume other embeddings could be manipulated into the same result, though it's not necessarily as obvious. $\endgroup$
    – Danica
    Commented Mar 14, 2016 at 15:02

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