Dual total correlation
In information theory, dual total correlation,[1] information rate,[2] excess entropy,[3][4] or binding information[5] is one of several known non-negative generalizations of mutual information.
While total correlation is bounded by the sum entropies of the n elements, the dual total correlation is bounded by the joint-entropy of the n elements.
Although well behaved, dual total correlation has received much less attention than the total correlation.
A measure known as "TSE-complexity" defines a continuum between the total correlation and dual total correlation.
[3] For a set of n random variables
{
, … ,
n
, the dual total correlation
is the joint entropy of the variable set
is the conditional entropy of variable
, given the rest.
The dual total correlation normalized between [0,1] is simply the dual total correlation divided by its maximum value
, Dual total correlation is non-negative and bounded above by the joint entropy
Secondly, Dual total correlation has a close relationship with total correlation,
, and can be written in terms of differences between the total correlation of the whole, and all subsets of size
Furthermore, the total correlation and dual total correlation are related by the following bounds: Finally, the difference between the total correlation and the dual total correlation defines a novel measure of higher-order information-sharing: the O-information:[7] The O-information (first introduced as the "enigmatic information" by James and Crutchfield[8] is a signed measure that quantifies the extent to which the information in a multivariate random variable is dominated by synergistic interactions (in which case
) or redundant interactions (in which case
Han (1978) originally defined the dual total correlation as, However Abdallah and Plumbley (2010) showed its equivalence to the easier-to-understand form of the joint entropy minus the sum of conditional entropies via the following:
