I am working on an Anomaly Detection Problem and the algorithm I used is an Autoencoder Multivariate Gaussian. The problem with my data is that it is unlabeled and not correlated.
For example, let's say I want to predict anomaly in my system and I have features like:
vibration, temp, pressure, catalyst
Out of these features, only vibration is a good indicator of an anomaly, but other features (e.g. temp, pressure and cat) are independent of vibration. So even if there is an anomaly, the vibration feature will only show us some indication, but not the rest of the features.
I built the above-mentioned algorithm! Autoencoder gave me 99% accuracy during training time, but during testing, it totally failed (similar case with MVG).
So, do I need correlated features or do these algorithms should work on this data? Please suggest any other approach to solve this kind of problem.