TCGF: A unified tensorized consensus graph framework for multi-view
  representation learning
Xiangzhu Meng Wei Wei Qiang Liu Shu Wu Liang Wang
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.