Semi-supervised Online Multiple Kernel Learning Algorithm for Big Data
Abstract: In order to improve
the performance of machine learning in big data, online multiple kernel
learning algorithms are proposed in this paper. First, a supervised online
multiple kernel learning algorithm for big data (SOMK_bd) is proposed to reduce
the computational workload during kernel modification. In SOMK_bd, the
traditional kernel learning algorithm is improved and kernel integration is
only carried out in the constructed kernel subset. Next, an unsupervised online
multiple kernel learning algorithm for big data (UOMK_bd) is proposed. In
UOMK_bd, the traditional kernel learning algorithm is improved to adapt to the
online environment and data replacement strategy is used to modify the kernel
function in unsupervised manner. Then, a semi-supervised online multiple kernel
learning algorithm for big data (SSOMK_bd) is proposed. Based on incremental
learning, SSOMK_bd makes full use of the abundant information of large scale
incomplete labeled data, and uses SOMK_bd and UOMK_bd to update the current
reading data. Finally, experiments are conducted on UCI data set and the
results show that the proposed algorithms are effective.
Author: Ning Liu, Jianhua Zhao
Journal Code: jptkomputergg160225
