TCGF: A unified tensorized consensus graph framework for multi-view representation learning

TL;DR

This paper introduces TCGF, a scalable and robust unified tensorized framework for multi-view representation learning that effectively captures multi-scale information and enhances data fusion across views.

Contribution

The paper proposes a universal multi-view learning framework that unifies existing methods, incorporates tensorization for multi-scale data, and introduces an efficient optimization strategy.

Findings

TCGF outperforms state-of-the-art methods on seven datasets.

It effectively captures multi-scale and multi-view information.

The framework is scalable for large datasets.

Abstract

Multi-view learning techniques have recently gained significant attention in the machine learning domain for their ability to leverage consistency and complementary information across multiple views. However, there remains a lack of sufficient research on generalized multi-view frameworks that unify existing works into a scalable and robust learning framework, as most current works focus on specific styles of multi-view models. Additionally, most multi-view learning works rely heavily on specific-scale scenarios and fail to effectively comprehend multiple scales holistically. These limitations hinder the effective fusion of essential information from multiple views, resulting in poor generalization. To address these limitations, this paper proposes a universal multi-view representation learning framework named Tensorized Consensus Graph Framework (TCGF). Specifically, it first provides a unified framework for existing multi-view works to exploit the representations for individual view, which aims to be suitable for arbitrary assumptions and different-scales datasets. Then, stacks them into a tensor under alignment basics as a high-order representation, allowing for the smooth propagation of consistency and complementary information across all views. Moreover, TCGF proposes learning a consensus embedding shared by adaptively collaborating all views to uncover the essential structure of the multi-view data, which utilizes view-consensus grouping effect to regularize the view-consensus representation. To further facilitate related research, we provide a specific implementation of TCGF for large-scale datasets, which can be efficiently solved by applying the alternating optimization strategy. Experimental results conducted on seven different-scales datasets indicate the superiority of the proposed TCGF against existing state-of-the-art multi-view learning methods.