Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained.We develop a proximal bundle optimization algorithm to globally solve the minmax optimization problem. Our empirical study shows the proposed subspace representation learning method can effectively facilitate multi-view clustering and induce superior clustering results than alternative multiview clustering methods. Copyright

27th AAAI Conference on Artificial Intelligence, AAAI 2013
School of Computer Science

Guo, Y. (2013). Convex subspace representation learning from multi-view data. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 (pp. 387–393).