Random projections: data perturbation for classification problems
This work addresses classification challenges in high-dimensional data, offering flexible solutions for large-scale statistical problems.
The paper examines random projections as a data perturbation technique for classification problems, highlighting two approaches: ensemble methods that aggregate multiple projections to improve statistical accuracy, and hashing/sketching techniques that reduce computational complexity while preserving statistical efficiency.
Random projections offer an appealing and flexible approach to a wide range of large-scale statistical problems. They are particularly useful in high-dimensional settings, where we have many covariates recorded for each observation. In classification problems there are two general techniques using random projections. The first involves many projections in an ensemble -- the idea here is to aggregate the results after applying different random projections, with the aim of achieving superior statistical accuracy. The second class of methods include hashing and sketching techniques, which are straightforward ways to reduce the complexity of a problem, perhaps therefore with a huge computational saving, while approximately preserving the statistical efficiency.