  Divergence functions play a key role as to measure the discrepancy between
two points in the field of machine learning, statistics and signal processing.
Well-known divergences are the Bregman divergences, the Jensen divergences and
the f-divergences. In this paper, we show that the symmetric Bregman divergence
can be decomposed into the sum of two types of Jensen divergences and the
Bregman divergence. Furthermore, applying this result, we show another sum
decomposition of divergence is possible which includes f-divergences
explicitly.
