@inproceedings{21953328fb7c45559bdf33bb6703311a,
title = "Incremental ELMVIS for Unsupervised Learning",
abstract = "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.",
keywords = "512 Business and Management, ELM, Visualization, Assignment problem, Unsupervised learning, Semi-supervised learning",
author = "Anton Akusok and Emil Eirola and Yoan Miche and Ian Oliver and Kaj-Mikael Bj{\"o}rk and Andrey Gritsenko and Stephen Baek and Amaury Lendasse",
year = "2017",
month = may,
day = "26",
doi = "10.1007/978-3-319-57421-9\_15",
language = "English",
isbn = "978-3-319-57420-2",
series = " Proceedings in Adaptation, Learning and Optimization (PALO)",
publisher = "Springer",
pages = "183--193 ",
booktitle = "Proceedings of ELM-2016",
address = "International",
}