Maximum Entropy Functions: Approximate Gacs-Korner for Distributed Compression

Abstract

Consider two correlated sources $X$ and $Y$ generated from a joint distribution $p_{X,Y}$. Their Gács-Körner Common Information, a measure of common information that exploits the combinatorial structure of the distribution $p_{X,Y}$, leads to a source decomposition that exhibits the latent common parts in $X$ and $Y$. Using this source decomposition we construct an efficient distributed compression scheme, which can be efficiently used in the network setting as well. Then, we relax the combinatorial conditions on the source distribution, which results in an efficient scheme with a helper node, which can be thought of as a front-end cache. This relaxation leads to an inherent trade-off between the rate of the helper and the rate reduction at the sources, which we capture by a notion of optimal decomposition. We formulate this as an approximate Gács-Körner optimization. We then discuss properties of this optimization, and provide connections with the maximal correlation coefficient, as well as an efficient algorithm, both through the application of spectral graph theory to the induced bipartite graph of $p_{X,Y}$.

Submitted to ITW 2016

References in corpus (3)

• Assisted Common Information with an Application to Secure Two-Party Sampling
• An Exploration of the Role of Principal Inertia Components in Information Theory
• Efficient Coding for Multi-source Networks using Gács-Körner Common Information

Cited by in corpus (2)

• Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering
• Applications of Common Information to Computing Functions