Fast Multi-view Privileged Random Vector Function Link Network

Abstract

In reality, feature data are usually obtained from different ways or levels for the same object, and the data obtained are called multi-view data. It is of research value to mine and utilize multi-view data and shows some advan-tages over traditional single-view learning. An important issue in multi-view learning (MVL) is how to meet the consistency between perspectives while maintaining complementarities between perspectives. In order to solve the above problem, based on the concepts including multi-view leaning and learning using privileged information (LUPI), a fast multi-view privileged RVFL (FMPRVFL) is proposed based on random vector function link network (RVFL) to accomplish multi-view classification tasks. The basic idea of FMPRVFL lies in the use of additional information from other views on average as the privileged information to supervise the classification in the current view. The objective function of FMPRVFL designed in such a mutually supervised manner can be optimized with analytical solutions, thus accelerating the training of FMPRVFL. It is revealed in the theoretical analysis that in contrast to classical multi-view learning, FMPRVFL can provide extra generalization capability. The results of an experiment on 64 datasets demonstrate that FMPRVFL outperforms other comparative methods both in average testing accuracy and running time.

Keywords

• |multi-view learning (mvl)|privileged information|random vector function link network (rvfl)