Methods and systems for reducing dimensionality in a reduction and prediction framework

Method and system that includes receiving an sensed data point from an industrial process; applying a mapping model to map the sensed data point to a respective embedding that has reduced dimensionality relative to the sensed data point; determining, based on a comparison of the respective embedding to prior embeddings, if the mapping model needs to be updated or not. When the mapping model needs to be updated, applying manifold learning to learn an updated set of model parameters for the mapping model. When the mapping model does not need to be updated, applying a classification model to the respective embedding to predict a classification for the sensed data point.