Comparing PCA and MRL for static models

Without reducing dimensionality, static models can be hundreds of MB large. Choosing the right dimensionality-reduction technique can shrink them without sacrificing retrieval quality. I was always a huge fan of Principal Component Analysis (PCA) for making static models smaller. For example, PCA is used in model2vec and was used in an older version of tokenlearn to post-process models, and is…