Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery

• URL: http://arxiv.org/abs/2507.12029v1
• Date: Wed, 16 Jul 2025 08:42:52 GMT
• Title: Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery
• Authors: Xinhang Wan, Jiyuan Liu, Qian Qu, Suyuan Liu, Chuyu Zhang, Fangdi Wang, Xinwang Liu, En Zhu, Kunlun He,
• Abstract summary: Novel class discovery (NCD) aims to cluster novel classes by leveraging knowledge from disjoint known classes.<n>We propose a novel framework named Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery (IICMVNCD)<n>IICMVNCD is the first attempt to explore NCD in multi-view setting so far.
• Score: 52.616615506638205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of novel class discovery (NCD), which aims to cluster novel classes by leveraging knowledge from disjoint known classes. While recent advances have made significant progress in this area, existing NCD methods face two major limitations. First, they primarily focus on single-view data (e.g., images), overlooking the increasingly common multi-view data, such as multi-omics datasets used in disease diagnosis. Second, their reliance on pseudo-labels to supervise novel class clustering often results in unstable performance, as pseudo-label quality is highly sensitive to factors such as data noise and feature dimensionality. To address these challenges, we propose a novel framework named Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery (IICMVNCD), which is the first attempt to explore NCD in multi-view setting so far. Specifically, at the intra-view level, leveraging the distributional similarity between known and novel classes, we employ matrix factorization to decompose features into view-specific shared base matrices and factor matrices. The base matrices capture distributional consistency among the two datasets, while the factor matrices model pairwise relationships between samples. At the inter-view level, we utilize view relationships among known classes to guide the clustering of novel classes. This includes generating predicted labels through the weighted fusion of factor matrices and dynamically adjusting view weights of known classes based on the supervision loss, which are then transferred to novel class learning. Experimental results validate the effectiveness of our proposed approach.

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