Multi-view kernel machine on single-view data

Existing multi-view learning focuses on the problem of how to learn from data represented by multiple independent sets of attributes (termed as multi-view data), and has been proved to bring an excellent performance. However, in general, we have only a single set of attributes (termed as single-view data) available. The goal of this paper is to employ the multi-view viewpoint to develop a multi-view kernel machine for such a single-view data. The key of doing so is to associate each learning machine with one kernel, take it as one view and thus form a set of learning machines from their corresponding kernels, as a result, a multi-view kernel machine can be developed by synthesizing them into a single learning framework. Further, in the two-view (two-kernel) case, we explore the relationship between the generalization ability of the proposed kernel machine and its associated kernels, in which with the kernel alignment (KA) as a correlation measure between kernels, it is found that superior performance of the proposed machine results from a weaker correlation between the constitutive kernels. To the best of our knowledge, both the multi-view learning on single-view data and the KA measure used here have not appeared in any literature. In practice, we take the kernel modified Ho–Kashyap with squared (KMHKS) approximation of the misclassification errors as a learning machine to develop a multi-view KMHKS (MultiV-KMHKS) on single-view data.