Abstract
Considering restricted Boltzmann machine (RBM) as an unsupervised pre-training phase, this paper delivers a study on predetermined model parameters in extreme learning machine (ELM). Because of the non-iterative attribute in fine-tuning phase, the property of hidden layer output plays an important part in model performance. For ELM, we give a theoretical analysis on the hidden layer parameters related to matrix perturbation and continuity of generalized inverse. Then by empirically analyzing the proposed RBM–ELM algorithm, we find that the impact of hidden layer parameters on generalization ability varies among the experimental datasets. By exploring the training process and comparing the model parameters between random assignment and RBM, we identify the special pattern of hidden layer output discussed in theoretical part and empirically show that such pattern could harm the model performance.
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