The representing measures can be determined in terms of the complex gamma function. We end by some simple considerations aiming at illuminating the signi- ficance of JSD-divergence. Consider the discrete case and introduce entropy as usual, i.e. H(P) = − n p n log p n (with p n...
In this section, we start from the Jensen-Shannon divergence and explore how this similarity measure can be used to construct a graph kernel method for two directed graphs. In particular, the kernel can be computed in terms of the entropies of two individual graphs and the entropy of their ...
Our first idea is to design a new feature for the prediction of DNA-binding sites in proteins which leverages the Jensen–Shannon divergence 𝕁𝕊𝔻(𝐩𝑘∥𝐩𝑛𝑑):=ℍ((𝐩𝑘+𝐩𝑛𝑑)/2)−(ℍ(𝐩𝑘)+ℍ(𝐩𝑛𝑑))/2.𝕁𝕊𝔻pkpnd:=ℍpk+pnd/2−...
These expressions can be used to compute the mean and variance of the Jensen–Shannon distance, as well as of the Jensen–Shannon divergence. The computational problem with these expectations are the values of k = nm + 1 and of N, because the number of categories of the multinomial distribut...