Incremental kernel minimum squared error (KMSE)

Abstract

Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not sparse, which may be harmful for real time applications. Current proposed algorithms show hindrances when dealing with sample flow learning systems. A significant improvement utilizes incremental learning, namely IKMSE, to achieve better sparsity however it is not enough. We propose two additional adaptive incremental learning algorithms, viz. CRKMSE and PRKMSE. CRKMSE adopts a complete reduction strategy while PRKMSE uses the partial reduction. Generally, PRKMSE is superior to CRKMSE in terms of training time. And PRKMSE owns a better real time than CRKMSE in the testing phase due to fewer significant nodes. Finally, a lot of experiments are reported to confirm our viewpoints in this paper.