Brief

This paper introduces bidirectional random projections as a method for Ordinary Least Squares (OLS) regression in a fixed design setting. The research develops an expected excess loss bound for OLS estimators constructed using these projections, comparing it to existing bounds. The findings suggest a potential gap in performance that scales with the dimensionality of the projected data and the ratio of sample sizes, with numerical results on real-world data supporting these implications. AI