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

2025-07-16

AI Summary

Existing novel class discovery (NCD) methods are confined to single-view data and rely on pseudo-label supervision for clustering unknown classes, rendering them vulnerable to noise and high-dimensional feature interference, with limited robustness. This work pioneers the extension of NCD to the multi-view setting and proposes the first multi-view NCD framework. It eliminates pseudo-label dependency by jointly modeling intra-view shared structures and inter-view correlations, enabling stable clustering of novel classes. Specifically, it employs matrix factorization to learn view-specific factor matrices alongside a shared basis matrix across views, and introduces a dynamic weighting mechanism to facilitate cross-view knowledge transfer. Extensive experiments on real-world multi-omics datasets demonstrate substantial improvements in both accuracy and robustness for unknown class identification. The proposed framework establishes a new paradigm for unsupervised novel class discovery in complex, heterogeneous multi-view data.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Discover novel classes in multi-view data
• Improve clustering stability beyond pseudo-labels
• Leverage intra-view and inter-view correlations

Innovation

• Matrix factorization for intra-view feature decomposition
• Weighted fusion of factor matrices for inter-view guidance
• Dynamic adjustment of view weights using supervision loss