Asy. Wong et al., RECURSIVE LEAST-SQUARES APPROACH TO COMBINING PRINCIPAL AND MINOR COMPONENT ANALYSES, Electronics Letters, 34(11), 1998, pp. 1074-1076
A novel approach for high-performance data compression using neural ne
tworks is proposed. After the principal components of the input vector
s are extracted, the error covariance matrix obtained in the recursive
least square training process is used to perform minor components pru
ning so that a higher compression ratio is achieved. Simulation result
s show that our method effectively combines principal and minor compon
ent analyses.