Learning All-In Collaborative Multiview Binary Representation for Clustering

doi:10.1109/TNNLS.2022.3202102

Learning All-In Collaborative Multiview Binary Representation for Clustering

2024-03-01

11

PRE

AI

DOI：10.1109/TNNLS.2022.3202102

Original

Original request for help

Save

Abstract

En 中文

Multiview clustering via binary representation has attracted intensive attention due to its effectiveness in handling large-scale multiple view data. However, these kind of clustering approaches usually ignore a very important potential high-order correlation in discrete representation learning. In this article, we propose a novel all-in collaborative multiview binary representation for clustering (AC-MVBC) framework, where multiview collaborative binary representation and clustering structure are learned in a joint manner. Specifically, using a new type of tensor low-rank constraint, the high-order collaborations, i.e., cross-view and inner view collaborations, can be effectively captured in our model. Moreover, by incorporating the Bregman discrepancy, the projective consistency among different views can be guaranteed to achieve a more powerful binary representation. An efficient optimization algorithm is also proposed to solve the objective function with fast convergence empirically. Experimental results on several challenge datasets demonstrate that the proposed method has achieved highly competent performance compared with the state-of-the-art multiview clustering (MVC) methods while maintaining low computational and memory requirements.

Keywords:

Collaboration

Tensors

Costs

Binary codes

Encoding

Task analysis

Optimization

All-in collaborative

binary representation

Bregman discrepancy

joint learning

multiview clustering (MVC)

tensor low-rank constraint

Learning All-In Collaborative Multiview Binary Representation for Clustering

Abstract

Journal

Organization