where is the Kullback–Leibler divergence from q to p. Viewing the Kullback–Leibler divergence as a measure of distance, the I-projection is the "closest" distribution to q of all the distributions in P.
This inequality can be interpreted as an information-geometric version of Pythagoras' triangle inequality theorem, where KL divergence is viewed as squared distance in a Euclidean space.
It is worthwhile to note that since and continuous in p, if P is closed and non-empty, then there exists at least one minimizer to the optimization problem framed above. Furthermore, if P is convex, then the optimum distribution is unique.
The reverse I-projection also known as moment projection or M-projection is
Since the KL divergence is not symmetric in its arguments, the I-projection and the M-projection will exhibit different behavior. For I-projection, will typically under-estimate the support of and will lock onto one of its modes. This is due to , whenever to make sure KL divergence stays finite. For M-projection, will typically over-estimate the support of . This is due to whenever to make sure KL divergence stays finite.
The concept of information projection can be extended to arbitrary statistical f-divergences and other divergences.
- K. Murphy, "Machine Learning: a Probabilistic Perspective", The MIT Press, 2012.
- F. Nielsen, "What is... an information projection?", AMS Notices, (65) 3, pp. 321–324, 2018
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