An optimization framework for combining multiple graphs
Hilmi E. Egilmez, Antonio Ortega, Onur G. Guleryuz, Jana Ehmann, Sekoon Yea · 2016
This paper introduces a novel framework for combining multiple weighted graphs into a single optimized weighted graph. In our framework, we first develop a statistical formulation for the graph combining problem with a maximum likelihood criterion, and derive its optimality conditions. We then use these conditions to formulate the deterministic graph combining problem and propose a solution. Our experimental results show that the proposed solution provides better modeling compared to the commonly used averaging method. The introduced framework has various applications in signal processing and machine learning.