Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views

TL;DR

This paper introduces a multi-view fuzzy logic system that leverages both visible and hidden data views to improve learning performance, addressing limitations of traditional multi-view algorithms that ignore hidden information.

Contribution

It proposes a novel multi-view fuzzy logic framework that integrates hidden and visible view information for enhanced learning, filling a gap in existing multi-view methods.

Findings

Demonstrated improved performance over traditional methods

Validated effectiveness through extensive experiments

Effectively captures intrinsic multi-view data information

Abstract

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multi-view dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multi-view learning algorithm can achieve better performance by cooperative learning on the multi-view data. However, existing multi-view approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multi-view data, or vice versa. To address this problem, this paper proposes a multi-view fuzzy logic system, which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness.