− | | + | Furthermore, in the literature<ref name="GJS_divergence">{{cite journal|author=Jianhua Lin|title=Divergence Measures Based on the Shannon Entropy|journal=IEEE TRANSACTIONS ON INFORMATION THEORY|volume=37|issue=1|page=145-151|year=1991}}</ref>, the author proposes a [[Generalized JS Divergence]] as follows:{{NumBlk|:| |
| JSD_{\pi}(P_1,P_2,\cdots,P_n)\equiv H(\sum_{i=1}^n\pi_iP_i)-\sum_{i=1}^n\pi_i H(P_i) | | JSD_{\pi}(P_1,P_2,\cdots,P_n)\equiv H(\sum_{i=1}^n\pi_iP_i)-\sum_{i=1}^n\pi_i H(P_i) |
− | |{{EquationRef|GJSD}}}}其中,[math]P_i,i\in[1,m][/math]为一组概率分布向量,m为它们的维度,而[math]\pi=(\pi_1,\pi_2,\cdots,\pi_n)[/math]为一组权重,并满足:[math]\pi_i\in[0,1],\forall i\in[1,n][/math]和[math]\sum_{i=1}^n\pi_i=1[/math]。 | + | |{{EquationRef|GJSD}}}}Among them, [math]P_i,i\in[1,m][/math]is a set of probability distribution vectors, m is their dimension, and [math]\pi=(\pi_1,\pi_2,\cdots,\pi_n)[/math] is a set of weights that satisfy:[math]\pi_i\in[0,1],\forall i\in[1,n][/math]和[math]\sum_{i=1}^n\pi_i=1[/math]. |