Parallel graph Laplacian for large datasets

Graph based Semi-supervised learning methods are more natural way of data representation and processing but inclusion of infinite unlabeled points leads to a dense matrix which precludes generalization. Moreover, a major problem of graph Laplacian regularization method is its inability to scale. The graph Laplacian computation load does not allow exploitation of the entire information contained in the unlabeled data in SSL. In this paper, we address the scalability issue which ails the graph Laplacian and iterated graph Laplacian regularization in graph-based semi-supervised learning via parallelization using MapReduce approach. MapReduce is a programming model that can be used for processing large data sets by distributing parallel computations and data storage across a distributed cluster of machines. By splitting data into small chunks, the algorithm mimics processing small sparse data matrix in place of dense matrix. Experiment results show that without dip in accuracy, we are able to use resources more efficiently by load balancing.