Handling Noise and Missing Values in Sensory Data

smartphone data [0]

Detecting Outliers

Distribution-Based Models

We assume measurements in the red area to be outliers [1]

Distance-Based Models

Example outliers for local outlier factor [1]

Imputation of Missing Values

A Combined Approach: The Kalman Filter

Kalman Filter example [8]

Transformation

LowPass Filter

Application of the filter on time series data [2]

Principal Component Analysis

Example dataset for principal component analysis [2]

Conclusion

The amazing book [1]

References

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PhD Candidate in Artificial Intelligence @ Vrije Universiteit Amsterdam. https://www.alessandrozonta.ml/

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Alessandro Zonta

Alessandro Zonta

PhD Candidate in Artificial Intelligence @ Vrije Universiteit Amsterdam. https://www.alessandrozonta.ml/

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