Handling noise and missing values in sensory data

Mark Hoogendoorn*, Burkhardt Funk

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

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In this chapter, approaches to remove noise, that is inherently present in sensory data, are introduced. This includes outlier detection algorithms, missing value imputation, as well as approaches to filter more subtle noise in the data including the low pass filter and principal component analysis. The Kalman filter is also explained to remove noise and impute missing values.

Original languageEnglish
Title of host publicationMachine Learning for the Quantified Self
Subtitle of host publicationOn the Art of Learning from Sensory Data
Number of pages26
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
Publication statusPublished - 2018

Publication series

NameCognitive Systems Monographs
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933


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