Dual Fuzzy Learning With Global–Local Structure Fusion for Scalable Multiview Subspace Clustering

Abstract

Anchor guidance mechanism has demonstrated strong scalability for clustering large-scale datasets, driving its widespread applications in multiview clustering in recent years. Accordingly, a series of anchor-based multiview subspace clustering methods have been proposed to improve computational efficiency. However, many of these methods suffer from three major limitations. First, they generally focus on the integrated or specific local structures, while ignoring global information. Second, the typical postprocessing with singular value decomposition and $k$-means incurs extra time burden and yields volatile clustering performance. Third, different views may contain noise and exhibit complex relationships, making crisp partitioning unreliable and thereby requiring robust, uncertainty-aware learning. To address these issues, a scalable dual fuzzy learning and structure fusion multiview subspace clustering (DFFMSC) framework is proposed. Inspired by the effectiveness of fuzzy clustering in handling uncertain data, we incorporate fuzzy learning to enable more flexible and robust cluster partitioning. Unlike many multiview fuzzy clustering methods involving intricate cooperative learning terms, we incorporate two concise fuzzy learning terms into a dual-facilitated fuzzy learning component. One integrates the separate compact representations to learn a unified fuzzy membership matrix, while the other simultaneously enhances it through global–local structure fusion learning. With a fast alternating optimization algorithm, the labels are directly obtained from the optimal fuzzy membership matrix. Overall, the proposed DFFMSC not only adequately fuses the local and global structures, but also achieves one-step clustering without postprocessing. Extensive experiments on nine popular multiview datasets validate its effectiveness and efficiency.