# Constrained Bilinear Factorization Multi-view Subspace Clustering

## TL;DR

This paper introduces CBF-MSC, a novel multi-view clustering method that leverages bilinear factorization with constraints to better exploit the shared information across views, outperforming existing methods.

## Contribution

It proposes a new bilinear factorization approach with orthonormality and low-rank constraints to fully utilize multi-view data's consensus information.

## Key findings

- Outperforms several state-of-the-art methods on nine benchmark datasets.
- Effectively captures shared clustering properties across multiple views.
- Demonstrates robustness and competitiveness in multi-view clustering tasks.

## Abstract

Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be uniform among multiple views. To this end, this paper proposes a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is imposed for all coefficient matrices to make them have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more fully. Finally, an Augmented Lagrangian Multiplier (ALM) based algorithm is designed to optimize the objective function. Comprehensive experiments tested on nine benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-arts.