# Can I normalize KL-divergence to be $\leq 1$?

The Kullback-Leibler divergence has a strong relationship with mutual information, and mutual information has a number of normalized variants. Is there some similar, entropy-like value that I can use to normalize KL-divergence such that the normalized KL-divergence is bounded above by 1 (and below by 0)?

• $1-\exp( -D_{KL} )$ suggests itself. – Emre Jul 14 '11 at 19:09
• @Emre I was hoping for something more along the lines of most of those MI examples where the measure is simply divided by some entropy-like calculation. – Michael McGowan Jul 14 '11 at 22:00
• To do that you would need to know the distribution maximally divergent from the reference. – Emre Jul 14 '11 at 22:14
• ...and the divergence is unbounded if one distribution has zero density where the other does not (think of the logarithm), so this seems impossible without further constraints. – Emre Jul 15 '11 at 17:02
• @Emre I think you've convinced me that it's likely impossible in the general case. It so happens I was primarily interested in cases where the two distributions share a particular relationship with one another, and with that I was able to find a bound (the situation you described cannot happen with my classes of distributions). I think your comments describing why this can't be done in the general case are the correct answer for the question as written, so if you want to write them into an answer then I can accept. – Michael McGowan Jul 15 '11 at 18:57

Instead you might consider normalization through a nonlinear transformation such as $1-\exp(-D_{KL})$.