  In this paper, novel dissensus algorithms based on the Oja principal
component analysis (PCA) flow are proposed to model opinion dynamics on the
unit sphere. The information of the covariance formed by the opinion state of
each agent is used to achieve a dissensus equilibrium on unsigned graphs. This
differs from most of the existing work where antagonistic interactions
represented by negative weights in signed graphs are used to achieve a
dissensus equilibrium. The nonlinear algorithm is analyzed under both constant
covariance and time-varying covariance leading to different behaviors.
Stability analysis for the unstable consensus and stable dissensus equilibria
is provided under various conditions. The performance of the algorithm is
illustrated through a simulation experiment of a multi-agent system.
