  Matrix sketching is a recently developed data compression technique. An input
matrix A is efficiently approximated with a smaller matrix B, so that B
preserves most of the properties of A up to some guaranteed approximation
ratio. In so doing numerical operations on big data sets become faster.
Sketching algorithms generally use random projections to compress the original
dataset and this stochastic generation process makes them amenable to
statistical analysis. The statistical properties of sketching algorithms have
been widely studied in the context of multiple linear regression. In this paper
we propose matrix sketching as a tool for rebalancing class sizes in supervised
classification with imbalanced classes. It is well-known in fact that class
imbalance may lead to poor classification performances especially as far as the
minority class is concerned.
