| citekey | SakuradaYairi2014Anomaly |
|---|---|
| source code | own |
| Learning type | semi-supervised |
| input | multivariate |
- AutoEncoder is trained on all non-anomaly data. Whenever it encounters an anomaly value, the reproduction error is quite higher than the error with non-anomaly instances.
- The test data is put into the AutoEncoder and the scores are returned.
- An assumption is made that all errors are normally distributed with some mean and std. Any error value that follows mean + kstd > threshold or mean - kstd < thereshold is considered as an anomaly. (NOT IMPLEMENTED)