Unsupervised embeders for data containing mostly noise.

I've got petabytes of 2D matrix data streaming in real-time. The data contains mostly noise, and rarely an event/gaussian (small, high intensity region in the matrix).

This data is currently unlabeled. I would like to create an unsupervised embedder that would create features from small window views of the data (i.e., random slices of fixed size).

This embedder will be used in later stages to cluster data, detect events, create new features for ML etc. This will also be useful …

create data data streaming deeplearning event features intensity matrix noise petabytes random real-time small streaming the matrix unsupervised