MapReduce based distributed learning algorithm for Restricted Boltzmann Machine

Deep learning is recently regarded as the closest artificial intelligence model to human brain. It is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. One deep model often consists of a hierarchical architecture that has the capability to model super non-linear and stochastic problems. Restricted Boltzmann Machine (RBM) is the main constructing block of current deep networks, as most of deep architectures are built with it. Based on MapReduce framework and Hadoop distributed file system, this paper proposes a distributed algorithm for training the RBM model. Its implementation and performance are evaluated on Big Data platform-Hadoop. The main contribution of the new learning algorithm is that it solves the scalability problem that limits the development of deep learning. The intelligence growing process of human brain requires learning from Big Data. The distributed learning mechanism for RBM makes it possible to abstract sophisticated and informative features from Big Data to achieve high-level intelligence. The evaluations of the proposed learning algorithm are carried out on image inpainting and classification problems based on the BAS dataset and MNIST hand-written digits dataset.