Jensen-Shannon Divergence JS-Divergence
Tags: #machine learningEquation
$$JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})$$Latex Code
JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})
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Introduction
Equation
Latex Code
JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})
Explanation
Latex code for the Jensen-Shannon Divergence(JS-Divergence). I will briefly introduce the notations in this formulation.
: KL Divergence between P and Q
: JS Divergence between P and Q, which is the symmetric divergence metric between distribution P and Q
: Distribution of P(x) over x
: Distribution of Q(x) over x
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