Signal Processing for Representation Learning: Theoretical Foundations and Emerging Applications

Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, compact, and transferable latent features from high-dimensional data. Despite remarkable empirical successes, existing representation learning methods lack interpretability, theoretical guarantees, and principled design, leading to the gap between theoretical foundation and practical applications.



Signal processing offers a rich set of mathematical tools - such as spectral analysis, filtering, sampling, frequency transform and stochastic modeling – which provides theoretical foundations for representation learning. On the other hand, the development of representation learning can stimulate the development of advanced signal processing in large-scale, geometric, and data-driven settings, especially in graph signal processing, manifold analysis and high-dimensional signal processing.



This research topic aims to foster the joint evolution of signal processing and machine learning, focusing on developing novel theoretical concepts and enabling analytical tools, which could further bridge the gap between signal processing and practical applications.



This research topic aims to advance the frontiers and integration of signal processing (SP) and modern artificial intelligence, with a particular emphasis on representation learning. It focuses on three key directions: 1) establishing theoretical principles for representation learning from a signal processing perspective; 2) developing novel signal processing concepts and methodologies inspired by interpretable and efficient representation learning; and 3) addressing real-world challenges and practical applications through SP-inspired representation learning approaches.



The collected contributions are expected to provide analytical, principled, and interpretable foundations for representation learning grounded in signal processing theory, while extending classical SP frameworks to non-Euclidean, large-scale, and data-driven settings. Submissions that demonstrate the impact of signal processing–based representation learning in practical applications are also encouraged, with the goal of bridging theoretical foundations and real-world deployment challenges.



Topics include, but are not limited to



1. Foundations and Theory



Spectral and frequency-domain analysis for representation learning



Stability, robustness, and generalization in representation learning



Information-theoretic and stochastic signal models for representation learning



AI-enabled model analysis and interpretability in representation learning



2. Graph and Non-Euclidean Representation Learning



Graph signal processing for graph representation learning



Frequency-aware graph neural networks and spectral graph learning



Learning on manifolds, simplicial complexes, multilayer networks, and hypergraphs



Dynamic and temporal graph representation learning



3. Data Privacy and Security for Representation Learning



Privacy-preserving signal processing for representation learning



Signal processing for resilient and robust representation learning



Federated representation learning and unlearning



4. Multimodal and Large-Scale Learning



Cross-modal signal alignment and representation learning



Spatio-temporal signal processing for representation learning



Scalable and lightweight representation learning



Foundation models and pre-training for multimodal signals



5. Practical Applications



Wireless communications and computer networks



Biomedical and brain signal analysis



Geoscience and remote sensing



Image, point cloud, and multispectral imaging processing



Autonomous systems and robotics perception



Acoustics, speech, and natural language audio processing.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Representation learning, Graph learning, Spectral analysis, Manifold learning, Self-Supervised Pre-training, Modality fusion, Model Interpretability

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.