Distance Estimation for Incomplete Data by Extreme Learning Machine

Emil Eirola, Anton Akusok, Kaj-Mikael Björk, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.
Original languageEnglish
Title of host publicationInternational Conference on Extreme Learning Machine: Proceedings of ELM-2017
Number of pages7
Place of PublicationCham
Publication date17.10.2018
ISBN (Print)978-3-030-01519-0
ISBN (Electronic)978-3-030-01520-6
Publication statusPublished - 17.10.2018
MoE publication typeA4 Article in conference proceedings
Event2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, China
Duration: 04.10.201707.10.2017

Publication series

NameProceedings in Adaptation, Learning and Optimization (PALO)
Volume 10


  • 512 Business and Management
  • Machine learning
  • Extreme learning machine
  • Incomplete data
  • Missing values
  • Distance estimation


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