Incremental ELMVIS for Unsupervised Learning

Anton Akusok, Emil Eirola, Yoan Miche, Ian Oliver, Kaj-Mikael Björk, Andrey Gritsenko, Stephen Baek, Amaury Lendasse

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM-2016
Antal sidor11
UtgivningsortCham
FörlagSpringer
Utgivningsdatum26.05.2017
Sidor183-193
ISBN (tryckt)978-3-319-57420-2
ISBN (elektroniskt)978-3-319-57421-9
DOI
StatusPublicerad - 26.05.2017
MoE-publikationstypA4 Artikel i en konferenspublikation

Publikationsserier

Namn Proceedings in Adaptation, Learning and Optimization (PALO)
Volym9

Nyckelord

  • 512 Företagsekonomi

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