Characterization of Linearly Separable Boolean Functions: A Graph-Theoretic Perspective

IEEE Trans Neural Netw Learn Syst. 2017 Jul;28(7):1542-1549. doi: 10.1109/TNNLS.2016.2542205. Epub 2016 Apr 5.

Abstract

In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function f of n variables into an induced subgraph Hf of the n -dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of Hf . We define a new class of graphs, called hyperstar, and prove that the induced subgraph Hf of any linearly separable Boolean function f is a hyperstar. The proposal of hyperstar helps us uncover a number of fundamental properties of linearly separable Boolean functions in this paper.