Known-Novel Relation Learning for Multi-view Novel Class Discovery

Novel Class Discovery (NCD) aims to cluster unlabeled samples from novel classes by transferring knowledge from labeled known classes, where the known and novel label spaces are disjoint. Existing NCD methods have achieved promising progress in single-view scenarios, but they are less effective for real-world multi-view data, in which different views provide both consistent and complementary information. Although recent multi-view NCD methods exploit distributional correlations between known and novel classes and perform adaptive view fusion, the similarity relations between known and novel categories remain insufficiently explored. In fact, novel classes are not isolated from known classes in feature or semantic spaces; their relation patterns with respect to known categories can provide valuable structural references for novel-class partitioning. To address this issue, this paper proposes a Known-Novel Relation Learning framework for Multi-view Novel Class Discovery. Specifically, we first employ multi-view matrix factorization to learn view-specific latent structures coupled by a shared partition representation. By constraining its known-class part with ground-truth labels, discriminative supervision is injected into multi-view representation learning and adaptive view-weight estimation, enabling distributional knowledge transfer to novel classes. Furthermore, known-class prototypes are regarded as relation anchors to construct a known-novel relation matrix, which represents novel samples in the reference space defined by known categories. By associating sample-level anchor relations with novel-class partitioning, samples assigned to the same novel class are encouraged to preserve consistent relation patterns, thereby explicitly transferring relational knowledge to novel-class clustering. These components are integrated into a unified optimization framework with an efficient iterative solver. Experiments on multiple multi-view datasets demonstrate the effectiveness of the proposed method.