# What is the difference between a kernel, and kernel (Gram) matrix?

Given a kernel, can we represent it as a Gram matrix? For example, a linear kernel can be presented (in Python/MATLAB code) in a Gram matrix as follows: K = X*X.T. If this is true, how to represent other non-trivial kernels in their Gram matrix form, e.g., check the following link, page 5, equation 17 showing Jensen-Shannon kernel: K(p,q) = exp(-JS(p||q))

• Can you briefly define (or point me to a definition) of "kernel", as you use the term? The paper you cite is pretty diffuse, full of examples and not precision. Sep 9, 2017 at 13:31
• You can look at this for the relation between the kernel trick, a Gram matrix (dot product matrix, for a given dataset) and the inner product in a high-dimensional vector space. @kimchilover Sep 9, 2017 at 16:53
• @kimchilover In my linked post I explain why (in machine learning) a kernel is any function $k: \mathbb{R}^n \times \mathbb{R}^n \to \mathbb{R}$ such that for any $x_i \in\mathbb{R}^n, i = 1 \ldots m$, the matrix $K_{ij} = k(x_i,x_j)$ is positive semi-definite. Sep 9, 2017 at 16:57
• This is not the good forum. Here we explain the maths behind. See stackoverflow and cs.stackexchange Sep 9, 2017 at 16:59
• So you are asking, is every positive semi-definite matrix a Gram matrix? Answer: of course. Sep 9, 2017 at 16:59

It seems you are talking about Kernels in the context of machine learning. In which case the difference between the Kernel and the Kernel \ Gram matrix can be understood via the following expression of Mercer's theorem:

### Mercer's theorem in the context of machine learning

Let $$X = \{x^{(1)}, ... , x^{(m)} \}$$ be a data set of $$m$$ points, each of which are $$n$$ dimensional vectors, i.e. $$x^{(i)} \in \mathbb{R}^n$$ then the function $$K$$ which maps $$K(x^{(i)},x^{(j)}) : \mathbb{R}^n \times \mathbb{R}^n \rightarrow \mathbb{R}$$

is a valid Kernel if and only if the matrix $$G$$, called the Kernel matrix, or Gram matrix is symmetric, positive semi-definite.

The matrix $$G$$ is an $$m \times m$$ matrix where each entry is the kernel of the corresponding data points.

$$G_{i,j} = K(x^{(i)}, x^{(j)})$$

Furthermore, note that

A function $$K(x,z)$$ is a valid kernel if it corresponds to an inner product in some (perhaps infinite dimensional) feature space.

Hence:

For the linear kernel, the Gram matrix is simply the inner product $$G_{i,j} = x^{(i) \ T} x^{(j)}$$. For other kernels, it is the inner product in a feature space with feature map $$\phi$$: i.e. $$G_{i,j} = \phi(x^{(i)})^T \ \phi(x^{(j)})$$

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