# Multi-view Regularized Gaussian Processes

## TL;DR

This paper introduces a novel multi-view Gaussian process model that regularizes marginal likelihood with posterior consistency across views, enhancing multi-view learning performance.

## Contribution

It proposes a new GP framework for multi-view learning that enforces consistency among views and introduces a point selection scheme to improve model effectiveness.

## Key findings

- Model outperforms existing multi-view learning methods.
- Point selection scheme improves predictive accuracy.
- Experimental results verify the model's effectiveness.

## Abstract

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.