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[2602.12982] Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation
Summary
The paper presents a novel approach to multi-dimensional visual data recovery using Scale-Aware Tensor Modeling and accelerated randomized computation techniques, enhancing efficiency and modeling capabilities.
Why It Matters
This research addresses significant limitations in existing multi-dimensional data recovery methods, particularly in computational efficiency. By introducing a generalized nonconvex regularization paradigm and advanced optimization algorithms, it has the potential to improve data processing across various applications in machine learning and computer vision.
Key Takeaways
- Introduces a FCTN-based generalized nonconvex regularization paradigm.
- Develops efficient optimization algorithms with convergence guarantees.
- Utilizes randomized compression algorithms to enhance computational efficiency.
- Demonstrates superiority over existing methods in visual quality and running time.
- Provides theoretical results on approximation error and convergence.
Computer Science > Machine Learning arXiv:2602.12982 (cs) [Submitted on 13 Feb 2026] Title:Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation Authors:Wenjin Qin, Hailin Wang, Jiangjun Peng, Jianjun Wang, Tingwen Huang View a PDF of the paper titled Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation, by Wenjin Qin and 4 other authors View PDF HTML (experimental) Abstract:The recently proposed fully-connected tensor network (FCTN) decomposition has demonstrated significant advantages in correlation characterization and transpositional invariance, and has achieved notable achievements in multi-dimensional data processing and analysis. However, existing multi-dimensional data recovery methods leveraging FCTN decomposition still have room for further enhancement, particularly in computational efficiency and modeling capability. To address these issues, we first propose a FCTN-based generalized nonconvex regularization paradigm from the perspective of gradient mapping. Then, reliable and scalable multi-dimensional data recovery models are investigated, where the model formulation is shifted from unquantized observations to coarse-grained quantized observations. Based on the alternating direction method of multipliers (ADMM) framework, we derive efficient optimization algorithms with convergence guarantees to solve the formulated models. To alleviate the computational b...
