Lie Group Machine Learning
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 156
Special Issue Editors
Interests: Lie group machine learning; cognitive software theory and methods; big data science and technology; artificial intelligence
Special Issue Information
Dear Colleagues,
Lie Group Machine Learning is an interdisciplinary research field combining Lie group theory with machine learning methods.
Lie groups are defined as a class of groups with the property of continuous transformation. Since created by the Norwegian mathematician S. Lie in the late 19th century, Lie group theory has been widely used in many fields such as physics, mechanics, chemistry, and robotics. Lie group machine learning uses the structural characteristics of Lie groups and has a unique advantage in processing data with transformation invariance. In 2004, a research team led by Prof. Fanzhang Li began to deeply study Lie group machine learning based on the geometric and algebraic properties of Lie groups. Since then, a series of Lie group machine learning algorithms have been designed, including Lie group deep structure learning, Lie group semi-supervised learning, and Lie group kernel learning, to name but a few. At present, Lie group machine learning has been widely and successfully used in many fields, such as robotics, computer vision, and natural language processing.
With the continuous development of artificial intelligence technology, Lie group machine learning gradually becomes an important branch of the machine learning field. In the future, research in this field will focus more on practical application scenarios and design more efficient, accurate, and interpretable machine learning models. At the same time, Lie group machine learning will keep injecting new vitality into the development of artificial intelligence by performing more in-depth crossovers and integration with other disciplines. Therefore, there are legitimate reasons to carry out special research on Lie group machine learning.
The scope of this Special Issue can be summarized but is not limited to the following keywords:
- Lie group algebraic learning;
- Lie group geometric learning;
- Lie group cover learning;
- Lie group meta-learning;
- Lie group continual learning;
- Lie group reinforcement learning;
- Lie group neural network learning;
- Lie group Bayesian learning;
- Symplectic group learning;
- Quantum group learning.
Prof. Dr. Fanzhang Li
Dr. Li Liu
Guest Editors
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Keywords
- Lie group algebraic learning
- Lie group geometric learning
- Lie group cover learning
- Lie group meta learning
- Lie group continual learning
- Lie group reinforcement learning
- Lie group neural network learning
- Lie group Bayesian learning
- symplectic group learning
- quantum group learning
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