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Matrix sketching is a recently developed data compression technique. An inputmatrix A is efficiently approximated with a smaller matrix B, so that Bpreserves most of the properties of A up to some guaranteed approximationratio. In so doing numerical operations on big data sets become faster.Sketching algorithms generally use random projections to compress the originaldataset and this stochastic generation process makes them amenable tostatistical analysis. The statistical properties of sketching algorithms havebeen widely studied in the context of multiple linear regression. In this paperwe propose matrix sketching as a tool for rebalancing class sizes in supervisedclassification with imbalanced classes. It is well-known in fact that classimbalance may lead to poor classification performances especially as far as theminority class is concerned.

Matrix sketching for supervised classification with imbalanced classes