Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization
This work addresses scalability issues in graph-based classification, but it is incremental as it builds on existing Laplacian regularization methods.
The authors tackled binary classification on large graphs by implementing a nonparametric Bayesian method with a truncated Laplacian prior, showing improved scalability compared to an untruncated version in simulated and real data examples.
This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis functions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data examples to illustrate the improved scalability in terms of size of the underlying graph.