A new method of moments for latent variable models
Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tenso...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ruffini, Matteo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Springer US, 1986, 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 |
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| Übergeordnetes Werk: |
volume:107 ; year:2018 ; number:8-10 ; day:22 ; month:05 ; pages:1431-1455 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10994-018-5706-4 |
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OLC2026528217 |
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| 520 | |a Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. | ||
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10.1007/s10994-018-5706-4 doi (DE-627)OLC2026528217 (DE-He213)s10994-018-5706-4-p DE-627 ger DE-627 rakwb eng 150 004 VZ Ruffini, Matteo verfasserin (orcid)0000-0003-0738-2198 aut A new method of moments for latent variable models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2018 Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. Spectral methods Method of moments Latent variable models Topic modeling Casanellas, Marta aut Gavaldà, Ricard aut Enthalten in Machine learning Springer US, 1986 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:107 year:2018 number:8-10 day:22 month:05 pages:1431-1455 https://doi.org/10.1007/s10994-018-5706-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 AR 107 2018 8-10 22 05 1431-1455 |
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10.1007/s10994-018-5706-4 doi (DE-627)OLC2026528217 (DE-He213)s10994-018-5706-4-p DE-627 ger DE-627 rakwb eng 150 004 VZ Ruffini, Matteo verfasserin (orcid)0000-0003-0738-2198 aut A new method of moments for latent variable models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2018 Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. Spectral methods Method of moments Latent variable models Topic modeling Casanellas, Marta aut Gavaldà, Ricard aut Enthalten in Machine learning Springer US, 1986 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:107 year:2018 number:8-10 day:22 month:05 pages:1431-1455 https://doi.org/10.1007/s10994-018-5706-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 AR 107 2018 8-10 22 05 1431-1455 |
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10.1007/s10994-018-5706-4 doi (DE-627)OLC2026528217 (DE-He213)s10994-018-5706-4-p DE-627 ger DE-627 rakwb eng 150 004 VZ Ruffini, Matteo verfasserin (orcid)0000-0003-0738-2198 aut A new method of moments for latent variable models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2018 Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. Spectral methods Method of moments Latent variable models Topic modeling Casanellas, Marta aut Gavaldà, Ricard aut Enthalten in Machine learning Springer US, 1986 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:107 year:2018 number:8-10 day:22 month:05 pages:1431-1455 https://doi.org/10.1007/s10994-018-5706-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 AR 107 2018 8-10 22 05 1431-1455 |
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10.1007/s10994-018-5706-4 doi (DE-627)OLC2026528217 (DE-He213)s10994-018-5706-4-p DE-627 ger DE-627 rakwb eng 150 004 VZ Ruffini, Matteo verfasserin (orcid)0000-0003-0738-2198 aut A new method of moments for latent variable models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2018 Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. Spectral methods Method of moments Latent variable models Topic modeling Casanellas, Marta aut Gavaldà, Ricard aut Enthalten in Machine learning Springer US, 1986 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:107 year:2018 number:8-10 day:22 month:05 pages:1431-1455 https://doi.org/10.1007/s10994-018-5706-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 AR 107 2018 8-10 22 05 1431-1455 |
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10.1007/s10994-018-5706-4 doi (DE-627)OLC2026528217 (DE-He213)s10994-018-5706-4-p DE-627 ger DE-627 rakwb eng 150 004 VZ Ruffini, Matteo verfasserin (orcid)0000-0003-0738-2198 aut A new method of moments for latent variable models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2018 Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. Spectral methods Method of moments Latent variable models Topic modeling Casanellas, Marta aut Gavaldà, Ricard aut Enthalten in Machine learning Springer US, 1986 107(2018), 8-10 vom: 22. Mai, Seite 1431-1455 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:107 year:2018 number:8-10 day:22 month:05 pages:1431-1455 https://doi.org/10.1007/s10994-018-5706-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 AR 107 2018 8-10 22 05 1431-1455 |
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Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. © The Author(s) 2018 |
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Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. © The Author(s) 2018 |
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Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the single-topic model and latent Dirichlet allocation, and we provide a tensor decomposition algorithm for the moments that proves to be robust both in theory and in practice. Experiments on synthetic data show that the proposed estimators outperform the existing ones in terms of reconstruction accuracy, and that the proposed tensor decomposition technique achieves the learning accuracy of the state-of-the-art method with significantly smaller running times. We also provide examples of applications to real-world text corpora for both single-topic model and LDA, obtaining meaningful results. © The Author(s) 2018 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2026528217</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503172312.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10994-018-5706-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2026528217</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10994-018-5706-4-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ruffini, Matteo</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0738-2198</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A new method of moments for latent variable models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We present an algorithm for the unsupervised learning of latent variable models based on the method of moments. 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