Large Tensor Analysis and Applications

Tensor analysis is a fundamental task for processing multi-dimensional data. This approach not only has ubiquitous applications in a range of different fields – including image processing, computer vision, and machine/deep learning – but also has its own theoretical foundations with the potential to nurture new methods with more advanced performance. However, the barriers between subject disciplines hinder communication between researchers with different research backgrounds. This lack of communication, to some extent, limits the power of state-of-the-art tensor analysis techniques in different fields, and restricts the development and applications of new and more powerful methods.

The goal of this Research Topic is to bring together the research challenges related to tensor analysis in different fields to break down the subject barriers and deliver interdisciplinary solutions and applications. Therefore, all contributions related to tensor analysis are welcome, including theoretical analyses, methodologies, algorithms, and applications in different fields.

Topics of interest include, but are not limited to:
- Tensor decomposition/factorization
- Tensor approximation
- Tensor regression
- Tensor analysis with statistical techniques
- Tensor analysis with deep neural networks
- Applications of tensor analysis in signal processing, image processing, and computer vision
- Applications of tensor analysis in artificial intelligence
- Other applications of tensor analysis

Keywords: tensor analysis, tensor application, signal processing, image processing, computer vision, machine learning, deep learning, artificial intelligence

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.