Handling noise and missing values in sensory data

Mark Hoogendoorn; Burkhardt Funk

doi:10.1007/978-3-319-66308-1_3

Handling noise and missing values in sensory data

Mark Hoogendoorn^*, Burkhardt Funk

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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Abstract

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 language	English
Title of host publication	Machine Learning for the Quantified Self
Subtitle of host publication	On the Art of Learning from Sensory Data
Publisher	Springer/Verlag
Pages	25-50
Number of pages	26
ISBN (Electronic)	9783319663081
ISBN (Print)	9783319663074
DOIs	https://doi.org/10.1007/978-3-319-66308-1_3
Publication status	Published - 2018

Publication series

Name	Cognitive Systems Monographs
Publisher	Springer
Volume	35
ISSN (Print)	1867-4925
ISSN (Electronic)	1867-4933

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10.1007/978-3-319-66308-1_3

Handling Noise and Missing Values in Sensory DataFinal published version, 1.24 MBLicence: Article 25fa Dutch Copyright Act

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Handling noise and missing values in sensory data

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