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

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 languageEnglish
Title of host publicationMachine Learning for the Quantified Self
Subtitle of host publicationOn the Art of Learning from Sensory Data
PublisherSpringer/Verlag
Pages25-50
Number of pages26
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameCognitive Systems Monographs
Volume35
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933

Fingerprint Dive into the research topics of 'Handling noise and missing values in sensory data'. Together they form a unique fingerprint.

Cite this