# Multivariate Jensen-Shannon divergence

This paper says multivariate Jensen-Shannon divergence is

$$JS(\mathbf{p}_1,\dots,\mathbf{p}_K) = \frac{1}{m} \sum KL(\mathbf{p}_i || \bar{\mathbf{p}})$$ with $$KL$$ being the KL-divergence of the multiple probability distributions. Is it accurate compared to bivariate JS-divergence?

Would a matrix of bivariate JS-divergences feasible (like the correlation matrix), and what would its diagonal consist of?

Besides these, if t-distributed Stochastic Neighbor Embedding (t-SNE) in sklearn.manifold.tsne can be used to form a representation of multivariate KL-divergence for minimization (multivariate rather than one pair at a time), can the same or another algorithm do the same for a multivariate version of Jensen-Shannon divergence?