A novel approach for high-performance data compression using neural networks is proposed. After the principal components of the input vectors are extracted, the error covariance matrix obtained in the recursive least square training process is used to perform minor components pruning so that a higher compression ratio is achieved. Simulation results show that our method effectively combines principal and minor component analyses.
