Constraint learning

In probability theory and statistics, the Jensen–Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius (IRad) or total divergence to the average. It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it is always a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.

Definition

Consider the set $M_{+}^1(A)$ of probability distributions where A is a set provided with some σ-algebra of measurable subsets. In particular we can take A to be a finite or countable set with all subsets being measurable.

The Jensen–Shannon divergence (JSD) $M_{+}^1(A)\times M_{+}^1(A)\to [0,\mathrm{\infty })$ is a symmetrized and smoothed version of the Kullback–Leibler divergence $D(P\parallel Q)$. It is defined by

$\mathrm{J}\mathrm{S}\mathrm{D}(P\parallel Q)=\frac{1}{2}D(P\parallel M)+\frac{1}{2}D(Q\parallel M)$

where $M=\frac{1}{2}(P+Q)$

If A is countable, a more general definition, allowing for the comparison of more than two distributions, is:

$\mathrm{J}\mathrm{S}\mathrm{D}(P_1,P_2,\dots ,P_n)=H\left(\sum _{i=1}^n{\pi }_i{P}_i\right)-\sum _{i=1}^n{\pi }_{i}H(P_i)$

where ${\pi }_1,{\pi }_2,\dots ,{\pi }_n$ are the weights for the probability distributions $P_1,P_2,\dots ,P_n$ and $H(P)$ is the Shannon entropy for distribution $P$. For the two-distribution case described above,

$P_1=P,P_2=Q,{\pi }_1={\pi }_2=\frac{1}{2}.$

Bounds

The Jensen–Shannon divergence is bounded by 1, given that one uses the base 2 logarithm.

$0\le \mathrm{J}\mathrm{S}\mathrm{D}(P\parallel Q)\le 1$

For log base e, or ln, which is commonly used in statistical thermodynamics, the upper bound is ln(2):

$0\le \mathrm{J}\mathrm{S}\mathrm{D}(P\parallel Q)\le \ln (2)$

Relation to mutual information

Jensen–Shannon divergence is the mutual information between a random variable $X$ from a mixture distribution $M=\frac{P+Q}{2}$ and a binary indicator variable $Z$ where $Z=1$ if $X$ is from $P$ and $Z=0$ if $X$ is from $Q$.

$\begin{array}{rl}I(X;Z)& =H(X)-H(X|Z)\\ & =-\sum M\log M+\frac{1}{2}[\sum P\log P+\sum Q\log Q]\\ & =-\sum \frac{P}{2}\log M-\sum \frac{Q}{2}\log M+\frac{1}{2}[\sum P\log P+\sum Q\log Q]\\ & =\frac{1}{2}\sum P(\log P-\log M)+\frac{1}{2}\sum Q(\log Q-\log M)\\ & =\mathrm{J}\mathrm{S}\mathrm{D}(P\parallel Q)\end{array}$

It follows from the above result that Jensen–Shannon divergence is bounded by 0 and 1 because mutual information is non-negative and bounded by $H(Z)=1$.

One can apply the same principle to the joint and product of marginal distribution (in analogy to Kullback–Leibler divergence and mutual information) and to measure how reliably one can decide if a given response comes from the joint distribution or the product distribution—given that these are the only possibilities.

Quantum Jensen–Shannon divergence

The generalization of probability distributions on density matrices allows to define quantum Jensen–Shannon divergence (QJSD). It is defined for a set of density matrices $({\rho }_1,\dots ,{\rho }_n)$ and probability distribution $\pi =({\pi }_1,\dots ,{\pi }_n)$ as

$\mathrm{Q}\mathrm{J}\mathrm{S}\mathrm{D}({\rho }_1,\dots ,{\rho }_n)=S\left(\sum _{i=1}^n{\pi }_i{\rho }_i\right)-\sum _{i=1}^n{\pi }_{i}S({\rho }_i)$

where $S(\rho )$ is the von Neumann entropy. This quantity was introduced in quantum information theory, where it is called the Holevo information: it gives the upper bound for amount of classical information encoded by the quantum states $({\rho }_1,\dots ,{\rho }_n)$ under the prior distribution $\pi$ (see Holevo's theorem) Quantum Jensen–Shannon divergence for $\pi =(\frac{1}{2},\frac{1}{2})$ and two density matrices is a symmetric function, everywhere defined, bounded and equal to zero only if two density matrices are the same. It is a square of a metric for pure states but it is unknown whether the metric property holds in general. The Bures metric is closely related to the quantum JS divergence; it is the quantum analog of the Fisher information metric.

Applications

The Jensen–Shannon divergence has been applied in bioinformatics and genome comparison, and in protein surface comparison.

Notes

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External links

• Ruby gem for calculating JS divergence
• Python code for calculating JS divergence