The paper introduces LieBN, an innovative framework geared towards batch normalization across Lie groups. This framework is particularly effective in normalizing both sample and population statistics, allowing for an improved understanding of data distributions on SPD manifolds. The authors further explore the generalization of Lie groups on SPD manifolds and present extensive experimental results that showcase the advantages of LieBN. Future research directions include extending LieBN to other types of Lie groups within machine learning, hinting at broad applicability and potential improvements in various computational tasks.
This paper proposes a novel framework called LieBN, enabling batch normalization over Lie groups, which effectively normalizes both the sample and population statistics.
Extensive experiments demonstrated that our LieBN framework showcases clear advantages over existing methods, specifically in the context of Lie groups on SPD manifolds.
The generalization of Lie groups on SPD manifolds is a significant advancement, as it opens up future avenues for extending LieBN to other Lie groups in machine learning.
The framework’s effectiveness is shown through rigorous testing, indicating that LieBN has the potential to improve performance in tasks related to Lie groups.
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