Dimensionality reduction is a well-known technique for improving space efficiency, typically applied uniformly across an entire dataset.
This paper investigates the possibilities of using dimensionality reduction techniques for subdomain representation compression.
We explore standard techniques such as Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) in image domains.
The results not only demonstrate the expected improvements in space and computation complexity crucial for edge-device ML applications but also show improvements in accuracy over direct full-embedding procedure.
One possible explanation is that dimensionality reduction effectively extracts subdomain features.
We also performed experiments to demonstrate transfer learning capabilities using the compressed representations.