This is a story of two constructions that actually end up being essentially the same thing, as I recently discovered.

1. Kernels

The story of kernels in machine learning goes somewhat like this:

Take a positive definite function $$K(x,y)$$ that captures some notion of similarity between objects (a standard example of such a kernel is the Gaussian $$K(x,y) = \exp(-\|x - y\|^2)$$). A positive definite function, btw, is like the generalization of a p.d matrix: the integral $$\int f(x)f(y)k(x,y)dx dy \ge 0$$.

You can then construct a Hilbert space that captures the structure of the kernel. Specifically, for a fixed set of points S, construct a vector space from the basis $$\{k_x | x \in S\}$$, where $$k_x(y) = K(x,y)$$, and then define an inner product of two vectors in this space in the usual way: If $$v_a = \sum a_x k_x$$ and $$v_b = \sum b_x k_x$$, then $$v_a \cdot v_b = \sum a_x b_y K(x,y)$$.

You get nice properties from this construction: the so-called reproducing property is one of them, which tells you that $$K(x,y) = k_x \cdot k_y$$. In other words, we can capture the similarity function K(x,y) by a "standard" inner product in a Hilbert space.

What's even neater is that by invoking Mercer's theorem, you can construct an orthogonal basis, and make sure that the Hilbert space is actually a Euclidean space. The squared Euclidean distance in this space can be written in kernel form, as

\[d_K(x,y) = K(x,x) + K(y,y) - 2K(x,y)\]

which is what you'd expect when treating K(.) as an inner product.

2. Negative-type distance.

The second story comes from Deza and Laurent. When can a distance function be embedded in Euclidean space ? It turns out that it's more convenient to talk about the square of the distance function, which we'll call D.

There's an elegant characterization of when D can be embedded isometrically into $$\ell^2_2$$: this can be done if and only if D satisfies the negative-type inequality:

\[ \sum b_i b_j D(x_i, x_j) \le 0, \sum b_i = 0\]

for all possible assignments to $$b_i$$.

The proof works via a construction called a covariance mapping that takes $D$ to the function $$k_D$$ defined as:

\[ k_D(x,y) = (1/2)(d(x, x_0) + d(y, x_0) - d(x,y)) \]

which differential geometry folks will recognize as the Gromov product.

The proof completes by showing that the negative-type condition on D implies positive definiteness of $$k_D$$, and this in turn means that $$k_D$$ can be expressed as an R-covariance:

\[ k_D(x,y) = \int f_x(\omega)f_y(\omega) d\mu(\omega) \]

for some measure space $\mu$.

Note that the RHS of the equation is an infinite-dimensional inner product.

3. Where the two stories come together

The mapping that takes a kernel to a distance is the inverse of the covariance mapping used to map a distance to a metric. In other words, if we take a kernel K, compute $$d_K$$, and then use the distance to kernel mapping to compute $$k_{d_K}$$, we get back K. Further, since we can show that the negative-type condition on D implies a positive-definite condition on $$k_D$$, we can start off with either "D satisfies negative-type inequalities" or "K is a positive definite kernel" and yield the same conclusion on $$\ell_2^2$$ embeddability.

What's interesting is that the two ways of thinking about this don't seem to have been connected together explicitly before.

p.s This is not a fully rigorous correspondence except possibly in the finite dimensional case, but I find that it's often convenient to replace the negative-type argument with a kernel positive-definiteness argument in my head.