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Random projections offer an appealing and flexible approach to a wide rangeof large-scale statistical problems. They are particularly useful inhigh-dimensional settings, where we have many covariates recorded for eachobservation. In classification problems there are two general techniques usingrandom projections. The first involves many projections in an ensemble -- theidea here is to aggregate the results after applying different randomprojections, with the aim of achieving superior statistical accuracy. Thesecond class of methods include hashing and sketching techniques, which arestraightforward ways to reduce the complexity of a problem, perhaps thereforewith a huge computational saving, while approximately preserving thestatistical efficiency.

Random projections: data perturbation for classification problems