Feature Bagging and Extreme Learning Machines: Machine Learning with Severe Memory Constraints

With the onset of easy access to supercomputers with high amounts of memory available, machine learning algorithms have continued to increase the resources necessary to perform their data analysis. This paper aims to show development in the other direction, by showing that through the use of a combination of feature bagging and ensembles of Extreme Learning Machines (ELMs) it is possible to leverage machine learning, without loss of accuracy, on devices where Flash memory is very scarce, and Random-access memory (RAM) is even scarcer, such as on embedded systems. This novel strategy is called Feature Bagged Extreme Learning Machines (FB-ELMs).