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Related Experiment Video

Updated: Jul 1, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

Wei Lan, Yinghao Guo, Qingfeng Chen

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 29, 2026

Summary

This summary is machine-generated.

This study introduces CAFE, a novel multi-view clustering method. CAFE enhances cluster alignment and improves false negative identification for robust performance across complete and incomplete multi-view data.

Related Experiment Videos

Last Updated: Jul 1, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Contrastive multi-view clustering methods leverage two-branch contrastive learning for high performance.
Existing methods often overlook cluster center alignment between fused and single views, impacting accuracy.
False negative identification strategies require further optimization in multi-view clustering.

Purpose of the Study:

To present a robust multi-view clustering method (CAFE) addressing cluster center alignment and false negative identification.
To enhance the coordination of consistent and complementary information across multiple views.
To refine cluster structures by systematically aligning single-view and fused-view cluster centers.

Main Methods:

Developed a cross-view adaptive fusion module with dual weights (view and sample levels) for effective information coordination.
Proposed a dual-driven cluster center enhancement framework with a dual alignment mechanism for refining cluster structures.
Introduced a second-order proximity graph embedding method to improve false negative rectification by analyzing neighborhood similarity.

Main Results:

CAFE demonstrates state-of-the-art performance on six widely used multi-view benchmark datasets.
The method achieves superior results in both complete and incomplete multi-view scenarios.
Experimental validation confirms the effectiveness of the proposed fusion and cluster center enhancement strategies.

Conclusions:

CAFE offers a robust solution for multi-view clustering by effectively addressing cluster center alignment and false negative identification.
The proposed adaptive fusion and dual-driven enhancement frameworks significantly improve clustering accuracy.
CAFE provides a promising approach for handling diverse multi-view data, including incomplete datasets.