Kernel embeddings for learning machines have proven themselves to achieve great performance through strong representational power. To leverage this performance, Lopez-Paz et al. introduced kernel-based embedding for feature construction in pairwise causal discovery.

Starting from the dataset of empirical distributions S = { (x_{ij},y_{ij}){j=1}^{n_i} }{i=1}^n, a kernel mean embedding allows to project all those empirical distributions into the same Reproducing Kernel Hilbert Space (RKHS) $\mathcal{H}_k$. To obtain a homogeneous and low dimension embedding, Lopez-Paz et al. uses random cosine based embeddings that approximate empirical kernel mean embeddings in low dimension: $\mu_{k,m}(P_{S_j}) \\= \frac{2C_k}{|S|} \sum_{x_{ij},y_{ij} \in S_j} (\cos (w_j^x \ast x_{ij} + w_j^y \ast y_{ij} + b_j))_{j=1}^m \in \mathbb{R}^m$ where $\{w_j,b_j\}_{j=1}^m$ are the kernel parameters sampled i.i.d. in $\mathbb{N}_{0,2} \times [0, 2\pi]$, as well as their number $m$ defining the number of dimensions of the output space, $P_S$ is the empirical distribution, and $C_k = \int_{\mathcal{L}} p_k(w)dw$, with p_k : mathbb{R}^d rightarrow mathbb{R} the positive and integrable Fourier transform of the chosen kernel $k$ , equal to 1 in this case.