# Proof of closeness of kernel functions under pointwise product

How can I prove that pointwise product of two kernel functions is a kernel function?

By point-wise product, I assume you mean that if $k_1(x,y), k_2(x,y)$ are both valid kernel functions, then their product

\begin{align} k_{p}( x, y) = k_1( x, y) k_2(x,y) \end{align}

is also a valid kernel function.

Proving this property is rather straightforward when we invoke Mercer's theorem. Since $k_1, k_2$ are valid kernels, we know (via Mercer) that they must admit an inner product representation. Let $a$ denote the feature vector of $k_1$ and $b$ denote the same for $k_2$.

\begin{align} k_1(x,y) = a(x)^T a(y), \qquad a( z ) = [a_1(z), a_2(z), \ldots a_M(z)] \\ k_2(x,y) = b(x)^T b(y), \qquad b( z ) = [b_1(z), b_2(z), \ldots b_N(z)] \end{align}

So $a$ is a function that produces an $M$-dim vector, and $b$ produces an $N$-dim vector.

Next, we just write the product in terms of $a$ and $b$, and perform some regrouping.

\begin{align} k_{p}(x,y) &= k_1(x,y) k_2(x,y) \\&= \Big( \sum_{m=1}^M a_m(x) a_m(y) \Big) \Big( \sum_{n=1}^N b_n(x) b_n(y) \Big) \\&= \sum_{m=1}^M \sum_{n=1}^N [ a_m(x) b_n(x) ] [a_m(y) b_n(y)] \\&= \sum_{m=1}^M \sum_{n=1}^N c_{mn}( x ) c_{mn}( y ) \\&= c(x)^T c(y) \end{align}

where $c(z)$ is an $M \cdot N$ -dimensional vector, s.t. $c_{mn}(z) = a_m(z) b_n(z)$.

Now, because we can write $k_p(x,y)$ as an inner product using the feature map $c$, we know $k_p$ is a valid kernel (via Mercer's theorem). That's all there is to it.

• How do you know that the feature Hilbert space is finite-dimensional? Couldn't it be even non-separable? – Andrei Kh Oct 20 '19 at 11:33
• @ViktorGlombik this is a valid conclusion, but should have a sentence of explanation. $k_p$ being representable as the inner product of two vectors obviously means that properties of the inner product transfer to the kernel $k_p$. The inner product is by definition symmetric and positive semi definite, hence so is $k_p$. – Doc Jul 23 '20 at 15:33

Assume $$K1$$ and $$K2$$ are the kernel matrix of these two kernel $$k_1(x,y)$$ and $$k_2(x,y)$$, respectively, and they are PSD. We define $$k(x,y) = k_1(x,y)k_2(x,y)$$ and want to prove it is also a kernel. This is equivalent to prove its corresponding kernel matrix $$K = K1 \circ K2$$ is PSD.
1. $$K_3 = K1 \otimes K2$$ is a PSD (The kronecker product of two PSD is PSD).
2. $$K$$ is a principal submatrix of $$K_3$$, and therefore is PSD (The principal submatrix of PSD is PSD).