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...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA
Cambridge University Press
2022
|
| Schlagwörter: | |
| 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 |
| 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. |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 30 Jun 2022) |
| Umfang: | 1 Online-Ressource (vi, 402 Seiten) |
| ISBN: | 9781009128490 |
| DOI: | 10.1017/9781009128490 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV048379624 | ||
| 003 | DE-604 | ||
| 005 | 20240216 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220727s2022 xx o|||| 00||| eng d | ||
| 020 | |a 9781009128490 |c Online |9 978-1-00-912849-0 | ||
| 024 | 7 | |a 10.1017/9781009128490 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9781009128490 | ||
| 035 | |a (OCoLC)1339076797 | ||
| 035 | |a (DE-599)BVBBV048379624 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 |a DE-92 |a DE-473 |a DE-83 |a DE-91 | ||
| 082 | 0 | |a 006.31 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 100 | 1 | |a Couillet, Romain |d 1983- |0 (DE-588)101863410X |4 aut | |
| 245 | 1 | 0 | |a Random matrix methods for machine learning |c Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology |
| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY, USA |b Cambridge University Press |c 2022 | |
| 300 | |a 1 Online-Ressource (vi, 402 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Title from publisher's bibliographic system (viewed on 30 Jun 2022) | ||
| 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. | ||
| 650 | 4 | |a Machine learning / Mathematics | |
| 650 | 4 | |a Matrix analytic methods | |
| 650 | 0 | 7 | |a Matrixverfahren |0 (DE-588)4169123-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Stochastische Matrix |0 (DE-588)4057624-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Matrixverfahren |0 (DE-588)4169123-4 |D s |
| 689 | 0 | 2 | |a Stochastische Matrix |0 (DE-588)4057624-3 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Liao, Zhenyu |d 1992- |0 (DE-588)1263697496 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-00-912323-5 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/9781009128490 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033758501 | |
| 966 | e | |u https://doi.org/10.1017/9781009128490 |l DE-12 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009128490 |l DE-634 |p ZDB-20-CBO |q BTU_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009128490 |l DE-92 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009128490 |l DE-91 |p ZDB-20-CBO |q TUM_Paketkauf_2022 |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009128490 |l DE-473 |p ZDB-20-CBO |q UBG_PDA_CBO |x Verlag |3 Volltext | |
Datensatz im Suchindex
| DE-BY-TUM_katkey | 2702723 |
|---|---|
| _version_ | 1821936518696009728 |
| any_adam_object | |
| author | Couillet, Romain 1983- Liao, Zhenyu 1992- |
| author_GND | (DE-588)101863410X (DE-588)1263697496 |
| author_facet | Couillet, Romain 1983- Liao, Zhenyu 1992- |
| author_role | aut aut |
| author_sort | Couillet, Romain 1983- |
| author_variant | r c rc z l zl |
| building | Verbundindex |
| bvnumber | BV048379624 |
| classification_rvk | ST 300 |
| collection | ZDB-20-CBO |
| ctrlnum | (ZDB-20-CBO)CR9781009128490 (OCoLC)1339076797 (DE-599)BVBBV048379624 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1017/9781009128490 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03468nam a2200541zc 4500</leader><controlfield tag="001">BV048379624</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240216 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220727s2022 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781009128490</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-00-912849-0</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781009128490</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9781009128490</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1339076797</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048379624</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-91</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.31</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Couillet, Romain</subfield><subfield code="d">1983-</subfield><subfield code="0">(DE-588)101863410X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Random matrix methods for machine learning</subfield><subfield code="c">Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, United Kingdom ; New York, NY, USA</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (vi, 402 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 30 Jun 2022)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning / Mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matrix analytic methods</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Matrixverfahren</subfield><subfield code="0">(DE-588)4169123-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Stochastische Matrix</subfield><subfield code="0">(DE-588)4057624-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Matrixverfahren</subfield><subfield code="0">(DE-588)4169123-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Stochastische Matrix</subfield><subfield code="0">(DE-588)4057624-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liao, Zhenyu</subfield><subfield code="d">1992-</subfield><subfield code="0">(DE-588)1263697496</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-00-912323-5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033758501</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="l">DE-12</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="l">DE-634</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BTU_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="l">DE-92</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">TUM_Paketkauf_2022</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009128490</subfield><subfield code="l">DE-473</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UBG_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048379624 |
| 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 |
| open_access_boolean | |
| owner | DE-12 DE-92 DE-473 DE-BY-UBG DE-83 DE-91 DE-BY-TUM |
| owner_facet | DE-12 DE-92 DE-473 DE-BY-UBG DE-83 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (vi, 402 Seiten) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO BTU_PDA_CBO ZDB-20-CBO FHN_PDA_CBO ZDB-20-CBO TUM_Paketkauf_2022 ZDB-20-CBO UBG_PDA_CBO |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Cambridge University Press |
| record_format | marc |
| 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 |
| work_keys_str_mv | AT couilletromain randommatrixmethodsformachinelearning AT liaozhenyu randommatrixmethodsformachinelearning |