Multi-view Collaborative Label Learning with Balanced Regularization

Anchor graph-based methods reduce computational complexity and improve scalability in multi-view clustering. However, existing approaches suffer from two major limitations: they treat anchor graphs merely as sample representations — leading to under- representation and thus losing fine-grained structural information — and they ignore the consistency between anchor distribution and sample distribution, which results in suboptimal and unstable performance. To address these issues, we propose a balanced multi-view collaborative label learning method based on anchor representation. Specifically, instead of using anchors to represent samples, we use samples to represent anchors, which enhances the representational power of anchors and effectively resolves the under-representation problem inherent in the former approach. In addition, we design an anchor balancing mechanism based on the Schatten p-norm, supported by theoretical analysis, to solve the distribution consistency problem between the anchor space and the sample space. Collaborative learning is achieved by alternately updating sample labels and anchor labels, while a low-rank tensor constraint is imposed on the sample labels to capture complementary information across views. Extensive experiments demonstrate the effectiveness and superiority of our method.