Advanced Tensor Learning and Analysis: Theory, Symmetry, and Applications
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 18
Special Issue Editors
Interests: machine learning; hyperspectral image processing; tensor and matrix decomposition
Interests: deep learning; remote sensing image restoration; fusion and interpretation
Special Issues, Collections and Topics in MDPI journals
Interests: tensors; statistics; machine learning
Special Issue Information
Dear Colleagues,
Tensors provide a natural and powerful framework for representing multi-dimensional data and modeling complex interactions across diverse scientific and engineering domains. In recent years, tensor-based learning and analysis have emerged as fundamental tools in modern data science, enabling the discovery of latent structures and symmetries in high-dimensional datasets. From signal processing and computer vision to scientific computing and machine learning, tensor methods are playing an increasingly critical role in both theoretical developments and practical applications.
Symmetry and structural properties are essential to many tensor models and algorithms. Exploiting these properties enables more efficient representations, improved interpretability, and stronger theoretical guarantees. Meanwhile, the rapid development of deep learning has created new opportunities for integrating tensor structures into neural network architectures, leading to more compact models, improved generalization, and enhanced computational efficiency.
This Special Issue will bring together researchers and practitioners working on the latest developments in tensor learning, tensor analysis, and their applications across disciplines. In particular, we welcome contributions that explore the role of symmetry and structure in tensor models, propose novel tensor decomposition paradigms, develop theoretical foundations for tensor learning, and demonstrate impactful applications in emerging data-driven fields.
Potential topics include, but are not limited to, the following:
- Novel paradigms for tensor decomposition and tensor factorization;
- Theoretical advances in tensor analysis, including identifiability, uniqueness, and optimization;
- Symmetry and structured representations in tensor learning;
- Tensor-based methods for high-dimensional data analysis;
- Integration of tensor models with deep neural networks;
- Tensor networks and their applications in machine learning and scientific computing;
- Efficient algorithms for large-scale tensor computation;
- Tensor methods in computer vision, signal processing, and remote sensing;
- Applications of tensor learning in healthcare, recommender systems, and natural language processing;
- Interdisciplinary applications of tensor analysis in science and engineering.
We encourage submissions that present novel theoretical insights, methodological innovations, or practical applications of tensor learning and analysis. Contributions that highlight the role of symmetry and structural properties in improving tensor models and algorithms are particularly welcome.
All submitted manuscripts will undergo a rigorous peer-review process to ensure their high scientific quality and relevance to the scope of this Special Issue.
We look forward to receiving your submissions and showcasing the latest advancements in tensor learning and analysis.
Dr. Jiangjun Peng
Dr. Shuang Xu
Dr. Hailin Wang
Dr. Tengyu Ji
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- tensor learning
- structured and symmetric tensor models
- tensor decomposition
- tensor methods in machine learning
- tensor-based data science applications
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