A MapReduce Cortical Algorithms Implementation for Unsupervised Learning of Big Data

Abstract

In the big data era, the need for fast robust machine learning techniques is rapidly increasing. Deep network architectures such as cortical algorithms are challenged by big data problems which result in lengthy and complex training. In this paper, we present a distributed cortical algorithm implementation for the unsupervised learning of big data based on a combined node-data parallelization scheme. A data sparsity measure is used to divide the data before distributing the columns in the network over many computing nodes based on the MapReduce framework. Experimental results on multiple datasets showed an average speedup of 8 . 1 × compared to serial implementations.