Random matrix methods for machine learning:
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world...
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA
Cambridge University Press
2022
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| Links: | https://doi.org/10.1017/9781009128490 |
| Zusammenfassung: | This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website. |
| Umfang: | 1 Online-Ressource (vi, 402 Seiten) |
| ISBN: | 9781009128490 |
Internformat
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| 100 | 1 | |a Couillet, Romain |d 1983- | |
| 245 | 1 | 0 | |a Random matrix methods for machine learning |c Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology |
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| 520 | |a This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website. | ||
| 700 | 1 | |a Liao, Zhenyu |d 1992- | |
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| spelling | Couillet, Romain 1983- Random matrix methods for machine learning Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology Cambridge, United Kingdom ; New York, NY, USA Cambridge University Press 2022 1 Online-Ressource (vi, 402 Seiten) txt c cr This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website. Liao, Zhenyu 1992- Erscheint auch als Druck-Ausgabe 9781009123235 |
| spellingShingle | Couillet, Romain 1983- Random matrix methods for machine learning |
| title | Random matrix methods for machine learning |
| title_auth | Random matrix methods for machine learning |
| title_exact_search | Random matrix methods for machine learning |
| title_full | Random matrix methods for machine learning Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology |
| title_fullStr | Random matrix methods for machine learning Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology |
| title_full_unstemmed | Random matrix methods for machine learning Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology |
| title_short | Random matrix methods for machine learning |
| title_sort | random matrix methods for machine learning |
| work_keys_str_mv | AT couilletromain randommatrixmethodsformachinelearning AT liaozhenyu randommatrixmethodsformachinelearning |