A KPCA and DEA model for region innovation efficiency

Abstract

In recent years, evaluating the region innovation activity has gained a renewed interest in both growth economists and trade economists. In this work, a two-stage architecture constructed by combining kernel principal component analysis (KPCA) and the data envelopment analysis (DEA) is proposed for evolution region innovation. In the first stage, KPCA is used as feature extraction. In the second stage, DEA is used to evolution region innovation efficiency. By examining the region innovation data, it is shown that the proposed method achieves is effective and feasible. And it provides a better estimate tool for the region innovation activity. It also provides a novel way for the evolution design of the other engineering. © 2011 Springer-Verlag Berlin Heidelberg.