An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The method keeps high speed of ELMVIS+ while allowing for much larger possible sample pools due to lower memory requirements. The extension is useful for reaching a better local optimum with greedy optimization of ELMVIS, and the data structure can be specified in semi-supervised optimization. The major new application of incremental ELMVIS is not to visualization, but to a general dataset processing. The method is capable of learning dependencies from non-organized unsupervised data -- either reconstructing a shuffled dataset, or learning dependencies in complex high-dimensional space. The results are interesting and promising, although there is space for improvements.
@article{arxiv.1912.08638,
title = {Incremental ELMVIS for unsupervised learning},
author = {Anton Akusok and Emil Eirola and Yoan Miche and Ian Oliver and Kaj-Mikael Björk and Andrey Gritsenko and Stephen Baek and Amaury Lendasse},
journal= {arXiv preprint arXiv:1912.08638},
year = {2019}
}