surv-anom-det-wsn/todo.md

# things to do

## methods to cover

* drift detection
  * maybe some more
* extreme learning https://www.researchgate.net/profile/Giovanni-Iacca/publication/262274757_Online_Extreme_Learning_on_Fixed-Point_Sensor_Networks/links/53df8fbb0cf2aede4b490cb3/Online-Extreme-Learning-on-Fixed-Point-Sensor-Networks.pdf
* non stationary data
* more statistical methods
* capitalization in subsectio headers
* as it's => as it is
* HE => THEY
* schreiben warum non-blind calibration scheiße ist
* the paper => they
* Chan et al. \cite{chan2012} proposes a solution to this problem, he develops two methods to approximate the eigenvalue decomposition by updating the state recursively and reusing large parts of the already done calculation, which reduces the computational complexity. They simulate this algorithm on existing data sets and find it outperforms existing PCA based solutions such as \cite{li2000, tien2004}. 
* GHA fehlt ergebnis
* extreme learning erster absatz ist kaputt
* extreme learning erweitern
* SVM rajesagrar cites are weird
* https://scihubtw.tw/10.1145/3134302.3134337

https://netlibrary.aau.at/obvuklhs/content/titleinfo/5395523/full.pdf
## proposed structure

* introduction
* definitions
* problem overview
* drift
  * blind
  * non-blind
* model based
  * statistical
  * density based
  * ...
* machine learning
  * SVM
  * PCA
  * GHA
  * extreme learning
  * ...
* further reading
  * non stationary data
  * 
* Conclusion