A BAYESIAN APPROACH FOR EXTREME LEARNING MACHINE-BASED SUBSPACE LEARNING

In this paper, we describe a supervised subspace learning method that combines Extreme Learning methods and Bayesian learning. We approach the standard Extreme Learning Machine algorithm from a probabilistic point of view, Subsequently and we devise a method for the calculation of the network target vectors for Extreme Learning Machine based neural network training that is based on a Bayesian model exploiting both the labeling information available for the training data and geometric class information in the feature space determined by the network's hidden layer outputs. We combine the derived subspace learning method with Nearest Neighbor-based classification and compare its performance with that of the standard ELM approach and other standard methods.