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.
|Title of host publication||Machine Learning for the Quantified Self|
|Subtitle of host publication||On the Art of Learning from Sensory Data|
|Number of pages||26|
|Publication status||Published - 2018|
|Name||Cognitive Systems Monographs|