Multi-directional laplacian pyramids for completion of missing data entries

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

Abstract

A common pre-processing task in machine learning is handling missing data entries, also known as imputation. Standard techniques use mean values, regression or optimization based techniques for predicting the missing data values. In this paper, a kernel based technique is utilized for imputing data in a multi-scale manner. The construction is based on Laplacian pyramids, which operate on the row and column spaces of the data in several scales. Experimental results demonstrate the approach on publicly available datasets, and highlight its simple computational construction and convergence stability.

Original languageEnglish
Title of host publicationESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages709-714
Number of pages6
ISBN (Electronic)9782875870742
StatePublished - 2020
Event28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium
Duration: 2 Oct 20204 Oct 2020

Publication series

NameESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Country/TerritoryBelgium
CityVirtual, Online
Period2/10/204/10/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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