Pruning least objective contribution in KMSE
Although kernel minimum squared error (KMSE) is computationally simple, i.e., it only needs solving a linear equation set, it suffers from the drawback that in the testing phase the computational efficiency decreases seriously as the training samples increase. The underlying reason is that the solution of Nave KMSE is represented by all the training samples in the feature space. Hence, in this paper, a method of selecting significant nodes for KMSE is proposed. During each calculation round, the presented algorithm prunes the training sample making least contribution to the objective function, hence called as PLOC-KMSE. To accelerate the training procedure, a batch of so-called nonsignificant nodes is pruned instead of one by one in PLOC-KMSE, and this speedup algorithm is named MPLOC-KMSE for short. To show the efficacy and feasibility of the proposed PLOC-KMSE and MPLOC-KMSE, the experiments on benchmark data sets and real-world instances are reported. The experimental results demonstrate that PLOC-KMSE and MPLOC-KMSE require the fewest significant nodes compared with other algorithms. That is to say, their computational efficiency in the testing phase is best, thus suitable for environments having a strict demand of computational efficiency. In addition, from the performed experiments, it is easily known that the proposed MPLOC-KMSE accelerates the training procedure without sacrificing the computational efficiency of testing phase to reach the almost same generalization performance. Finally, although PLOC and MPLOC are proposed in regression domain, they can be easily extended to classification problem and other algorithms such as kernel ridge regression.
minimum squared error significant nodes regression classification kernel learning