Robust semi-supervised multi-view graph learning with sharable and individual structure. (August 2023) - Guo, Wei; Wang, Zhe; Du, Wenli
Highlights: We propose a novel semi-supervised multi-view learning framework by joint learning both individual and sharable subspace representation, which explores the complementary and common information of multi-view data in a reasonable manner and directly propagates the label information to unlabeled samples. We automatically recover clean data from original multi-view data and learn only subspace representations from the recovered multi-view clean data during the learning process, which improves the robustness of the proposed learning framework against the noise. To the best of our knowledge, this is the first attempt to learn self-representation subspace on the recovered clean multi-view data. We derive an effective optimization algorithm with strong convergence properties for the proposed learning framework. Extensive experiments on various real-world multi-view datasets demonstrate the feasibility and superiority of the proposed learning framework. Abstract: The construction of a high-quality multi-view consensus graph is key to graph-based semi-supervised multi-view learning (GSSMvL) methods. However, most existing GSSMvL methods explore sample relationships in the original multi-view feature space, which obtains a contaminated graph that cannot reveal the underlying manifold structure of the samples. Moreover, traditional GSSMvL methods fail to explore the diverse structures of multi-view features, which may lose their complementary information and lead to a suboptimal graph. In this paper, we propose a novel unified robust semi-supervised multi-view graph learning framework based on the sharable and individual structure (RSSMvSI), which can eliminate the influence of noise and exploit the knowledge of multi-view data in a reasonable manner. Specifically, we first learn clean data by manipulating sparse noise with l 2, 1 norm. We then simultaneously explore the sharable and individual self-representation subspace on the learned clean multi-view data. The key point is that noisy data does not participate in subspace learning, which improves the robustness of the proposed method. By constructing the optimal consensus graph with the learned sharable and individual subspace, RSSMvSI can better utilize the complementary information of multi-view data and approximate the manifold structure of samples. To the best of our knowledge, this is the first attempt to learn the self-representation subspace on recovered multi-view clean data. Extensive experiments on various real-world multi-view datasets demonstrate the superiority and robustness against state-of-the-art methods.