A Fast MBO Scheme for Multiclass Data Classification

We describe a new variant of the MBO scheme for solving the semi-supervised data classification problem on a weighted graph. The scheme is based on the minimization of the graph heat content energy. The resulting algorithms guarantee dissipation of the graph heat content energy for an extremely wide class of weight matrices. As a result our method is both flexible and unconditionally stable. Experimental results on benchmark machine learning datasets shows that our approach matches or exceeds the performance of current state of the art variational methods while being considerably faster.