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cross-view feature aggregation

Cross-view feature aggregation is a machine learning process that combines and integrates feature representations extracted from multiple distinct perspectives, modalities, or observational views of the same underlying entities into a unified representation. By merging these diverse sources of information, the process identifies and blends complementary details while capturing the common semantic structures and consensus patterns shared across different views. This synthesized representation helps resolve view-specific discrepancies, mitigates the effect of missing or noisy data from individual viewpoints, and provides a richer, more robust basis for downstream tasks such as multi-view clustering, classification, and retrieval.

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GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering

GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering

Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, Weisi Lin

OrganizationsChina University of GeosciencesNanyang Technological UniversityShenzhen UniversityYantai University

Why you should read this

Proposes a global and cross-view feature aggregation framework that integrates transformer-based sample relationships with structure-guided contrastive learning to boost multi-view clustering performance on both complete and incomplete datasets.

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary of similar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.

Added

2026-09-26