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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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY, USA
Cambridge University Press
2022
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| Subjects: | |
| Links: | https://doi.org/10.1017/9781009128490 https://doi.org/10.1017/9781009128490 https://doi.org/10.1017/9781009128490 https://doi.org/10.1017/9781009128490 https://doi.org/10.1017/9781009128490 https://doi.org/10.1017/9781009128490 |
| Summary: | 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. |
| Item Description: | Title from publisher's bibliographic system (viewed on 30 Jun 2022) |
| Physical Description: | 1 Online-Ressource (vi, 402 Seiten) |
| ISBN: | 9781009128490 |
| DOI: | 10.1017/9781009128490 |
Staff View
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| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:43:17Z |
| institution | BVB |
| isbn | 9781009128490 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033758501 |
| oclc_num | 1339076797 |
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| physical | 1 Online-Ressource (vi, 402 Seiten) |
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| publishDate | 2022 |
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| publisher | Cambridge University Press |
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| spellingShingle | Couillet, Romain 1983- Liao, Zhenyu 1992- Random matrix methods for machine learning Machine learning / Mathematics Matrix analytic methods Matrixverfahren (DE-588)4169123-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Stochastische Matrix (DE-588)4057624-3 gnd |
| subject_GND | (DE-588)4169123-4 (DE-588)4193754-5 (DE-588)4057624-3 |
| 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 |
| topic | Machine learning / Mathematics Matrix analytic methods Matrixverfahren (DE-588)4169123-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Stochastische Matrix (DE-588)4057624-3 gnd |
| topic_facet | Machine learning / Mathematics Matrix analytic methods Matrixverfahren Maschinelles Lernen Stochastische Matrix |
| url | https://doi.org/10.1017/9781009128490 |
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