Abstract
Extreme learning machine (ELM), as a new learning method for training feedforward neural networks, has shown its good generalization performance in regression and classification applications. Random projection (RP), as a simple and powerful technique for dimensionality reduction, is used for projecting high-dimensional data into low-dimensional subspaces while ensuring that the distances between data points are approximately preserved. This paper presents a systematic study on the application of RP in conjunction with ELM for both low- and high-dimensional data classification and clustering.
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Alshamiri, A.K., Singh, A., Surampudi, B.R. (2015). Combining ELM with Random Projections for Low and High Dimensional Data Classification and Clustering. In: Ravi, V., Panigrahi, B., Das, S., Suganthan, P. (eds) Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015). Advances in Intelligent Systems and Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-27212-2_8
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