Harnessing Low-Dimensional Structures in Machine Learning and Signal Processing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 21 December 2026 | Viewed by 1186
Special Issue Editors
Interests: machine learning; signal processing; numerical optimization; network science; high-dimensional statistics
Interests: information and coding theory; machine learning; wireless communications; signal and image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Background:
The exponential growth in data generation has revealed the following fundamental insight: While modern datasets may exist in extremely high-dimensional spaces, they often possess underlying low-dimensional structures that can be identified and leveraged for improved analysis. From sparse representations in signal processing to low-rank adaptations in large language models, from neural network compression to latent diffusion models, the recognition and exploitation of these intrinsic structures have become central to advancing both theoretical understanding and practical performance. This paradigm shift has driven remarkable progress, enabling breakthrough results in compressed sensing, matrix completion, image reconstruction, and scalable AI systems through the principled integration of structural assumptions with sophisticated optimization techniques.
Aims:
This special issue showcases cutting-edge research advancing the understanding and application of low-dimensional structures across machine learning and signal processing. We seek theoretical advances, novel algorithms, and compelling applications. Topics include, but are not limited to, theoretical foundations such as sample complexity analysis, information-theoretic bounds, and nonconvex optimization theory; algorithmic advances in sparse, low-rank, and tensor-based methods; and applications spanning computational imaging, recommender systems, wireless communications, compressed deep learning models, and other data-intensive domains where low-dimensional structures enable efficient solutions.
Dr. Jiaxi Ying
Prof. Dr. Jun Chen
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. Entropy 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 2600 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
- low-dimensional structures
- sparse representations
- low-rank models
- tensor decomposition
- compressed sensing
- nonconvex optimization
- information-theoretic bounds
- neural network compression
- efficient AI systems
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