I have been building a model to find explanation of Outliers in a high dimensional numerical data, generated from many sensors. The data contains more than 350 different fields and each field has numerical values (Float or Integer). It looks like: 350 columns and many rows. I want to find the outliers/ anomalies in the data and also the Explanation why those values are outliers.
I was reading about Generative models and found that "They have the potential to understand and explain the underlying structure of the Input data even when there are no labels." I would like to know if it will be good to use the GANs for outlier detection and explanation on numerical data?