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International Journal of Machine Learning and Cybernetics
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29-01-2024 | Original Article

Tensor discriminant analysis on grassmann manifold with application to video based human action recognition

Authors: Cagri Ozdemir, Randy C. Hoover, Kyle Caudle, Karen Braman

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2024

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Abstract

The article introduces a novel method called Tensor Grassmann Discriminant Analysis (TGDA) for human action recognition, utilizing the 3-mode t-SVD and a new tensor Grassmann projection kernel. TGDA captures spatial and temporal information from video data, embedding subspaces into a Hilbert space for discriminant analysis. The method outperforms existing techniques like GDA and GGDA, showcasing its effectiveness through extensive experimental results on well-known datasets. The article also provides a detailed mathematical foundation for the proposed methods, making it a valuable resource for researchers and practitioners in the field of computer vision and machine learning.
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Metadata
Title
Tensor discriminant analysis on grassmann manifold with application to video based human action recognition
Authors
Cagri Ozdemir
Randy C. Hoover
Kyle Caudle
Karen Braman
Publication date
29-01-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02096-5

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