将散度求和分解为三个散度

T. Nishiyama
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引用次数: 1

摘要

散度函数在机器学习、统计学和信号处理等领域中,对于度量两点之间的差异起着关键作用。众所周知的散度是Bregman散度,Jensen散度和f散度。本文证明了对称Bregman散度可以分解为两类Jensen散度和Bregman散度的和。进一步,应用这一结果,我们证明了另一种散度和分解是可能的,它显式地包含了f-散度。
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Sum decomposition of divergence into three divergences
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.
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