Multi-view Regularized Gaussian Processes
This work addresses the limited use of Gaussian processes in multi-view learning, offering a novel method that could benefit applications requiring integration of multiple data views, though it appears incremental in advancing existing GP frameworks.
The authors tackled the problem of applying Gaussian processes to multi-view learning by introducing a new model that regularizes marginal likelihood with consistency among posterior distributions from different views, and they demonstrated its effectiveness on multiple real-world datasets with performance improvements from a novel point selection scheme.
Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme.