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Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 29, 2026

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This study introduces ATTMVC, a novel framework for scalable multi-view clustering. It aligns anchor graphs before tensorization, improving cross-view consistency and clustering performance.

Area of Science:

Machine Learning
Data Mining
Computer Vision

Background:

Multi-view clustering aims to leverage data from multiple sources.
Existing tensor-based methods face scalability and structural consistency issues.
Limitations include high computational complexity and inconsistent cross-view representations.

Purpose of the Study:

To propose a novel, scalable multi-view clustering framework (ATTMVC).
To address limitations of existing tensorial methods in scalability and cross-view consistency.
To enhance the modeling of high-order correlations in multi-view data.

Main Methods:

Developed an anchor-based graph learning framework for efficient reconstruction.
Introduced cross-view anchor alignment into a shared latent space.

Related Experiment Videos

Employed a Threshold Tensor Rank (TTR) surrogate for improved low-rank regularization.

Main Results:

ATTMVC significantly reduces computational complexity compared to existing methods.
The proposed alignment strategy enforces structural consistency across views.
Experiments show ATTMVC outperforms state-of-the-art multi-view clustering algorithms.

Conclusions:

ATTMVC offers a scalable and effective solution for multi-view clustering.
The framework enhances cross-view comparability and high-order correlation modeling.
The study provides a publicly available implementation for reproducibility.