Survey on Lie Group Machine Learning
Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020011 |
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| author | Mei Lu Fanzhang Li |
| author_facet | Mei Lu Fanzhang Li |
| author_sort | Mei Lu |
| collection | DOAJ |
| description | Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research. |
| format | Article |
| id | doaj-art-dcdfe549d1b3499aa8ddbfe6afe628c6 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-dcdfe549d1b3499aa8ddbfe6afe628c62025-02-02T05:59:18ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-12-013423525810.26599/BDMA.2020.9020011Survey on Lie Group Machine LearningMei Lu0Fanzhang Li1<institution>School of Software Engineering, Jinling Institute of Technology</institution>, <city>Nanjing</city> <postal-code>211169</postal-code>, <country>China</country> and is also with the <institution>School of Computer Science and Technology, Jiangsu Normal University</institution>, <city>Xuzhou</city> <postal-code>221000</postal-code>, <country>China</country><institution>School of Computer Science and Technology, Soochow University</institution>, <city>Suzhou</city> <postal-code>215006</postal-code>, <country>China</country>Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research.https://www.sciopen.com/article/10.26599/BDMA.2020.9020011lie group machine learninglie group subspace orbit generation learningquantum group learningsymplectic group learninglie group fiber bundle learning |
| spellingShingle | Mei Lu Fanzhang Li Survey on Lie Group Machine Learning Big Data Mining and Analytics lie group machine learning lie group subspace orbit generation learning quantum group learning symplectic group learning lie group fiber bundle learning |
| title | Survey on Lie Group Machine Learning |
| title_full | Survey on Lie Group Machine Learning |
| title_fullStr | Survey on Lie Group Machine Learning |
| title_full_unstemmed | Survey on Lie Group Machine Learning |
| title_short | Survey on Lie Group Machine Learning |
| title_sort | survey on lie group machine learning |
| topic | lie group machine learning lie group subspace orbit generation learning quantum group learning symplectic group learning lie group fiber bundle learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020011 |
| work_keys_str_mv | AT meilu surveyonliegroupmachinelearning AT fanzhangli surveyonliegroupmachinelearning |