Convex subspace representation learning from multi-view data
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
|Conference||27th AAAI Conference on Artificial Intelligence, AAAI 2013|
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).