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Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties. / Berikov, Vladimir; Litvinenko, Alexander.

Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. ed. / M. Papadrakakis; V. Papadopoulos; G. Stefanou. National Technical University of Athens, 2019. p. 229-242 (Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Berikov, V & Litvinenko, A 2019, Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties. in M Papadrakakis, V Papadopoulos & G Stefanou (eds), Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019, National Technical University of Athens, pp. 229-242, 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019, Crete, Greece, 24.06.2019. https://doi.org/10.7712/120219.6338.18377

APA

Berikov, V., & Litvinenko, A. (2019). Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties. In M. Papadrakakis, V. Papadopoulos, & G. Stefanou (Eds.), Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 (pp. 229-242). (Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019). National Technical University of Athens. https://doi.org/10.7712/120219.6338.18377

Vancouver

Berikov V, Litvinenko A. Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties. In Papadrakakis M, Papadopoulos V, Stefanou G, editors, Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. National Technical University of Athens. 2019. p. 229-242. (Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019). doi: 10.7712/120219.6338.18377

Author

Berikov, Vladimir ; Litvinenko, Alexander. / Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties. Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. editor / M. Papadrakakis ; V. Papadopoulos ; G. Stefanou. National Technical University of Athens, 2019. pp. 229-242 (Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019).

BibTeX

@inproceedings{1cc5d35e9e0c4b6886ebc61f7ea69033,
title = "Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties",
abstract = "In this paper, we solve a semi-supervised regression problem. Due to the luck of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of the ensemble is calculated on both labeled and unlabeled data; this matrix is used as a similarity matrix in the regularization framework to derive the predicted outputs. We use the low-rank decomposition of the co-association matrix to significantly speedup calculations and reduce memory. Numerical experiments using the Monte Carlo approach demonstrate robustness, efficiency, and scalability of the proposed method.",
keywords = "Cluster ensemble, Co-association matrix, Graph Laplacian regularization, Hierarchical matrices, Low-rank matrix decomposition, Semi-supervised regression",
author = "Vladimir Berikov and Alexander Litvinenko",
note = "Publisher Copyright: {\textcopyright} 2019 Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 ; Conference date: 24-06-2019 Through 26-06-2019",
year = "2019",
doi = "10.7712/120219.6338.18377",
language = "English",
isbn = "9786188284494",
series = "Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019",
publisher = "National Technical University of Athens",
pages = "229--242",
editor = "M. Papadrakakis and V. Papadopoulos and G. Stefanou",
booktitle = "Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019",

}

RIS

TY - GEN

T1 - Semi-supervised regression using cluster ensemble and low-rank co-association matrix decomposition under uncertainties

AU - Berikov, Vladimir

AU - Litvinenko, Alexander

N1 - Publisher Copyright: © 2019 Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019

Y1 - 2019

N2 - In this paper, we solve a semi-supervised regression problem. Due to the luck of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of the ensemble is calculated on both labeled and unlabeled data; this matrix is used as a similarity matrix in the regularization framework to derive the predicted outputs. We use the low-rank decomposition of the co-association matrix to significantly speedup calculations and reduce memory. Numerical experiments using the Monte Carlo approach demonstrate robustness, efficiency, and scalability of the proposed method.

AB - In this paper, we solve a semi-supervised regression problem. Due to the luck of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of the ensemble is calculated on both labeled and unlabeled data; this matrix is used as a similarity matrix in the regularization framework to derive the predicted outputs. We use the low-rank decomposition of the co-association matrix to significantly speedup calculations and reduce memory. Numerical experiments using the Monte Carlo approach demonstrate robustness, efficiency, and scalability of the proposed method.

KW - Cluster ensemble

KW - Co-association matrix

KW - Graph Laplacian regularization

KW - Hierarchical matrices

KW - Low-rank matrix decomposition

KW - Semi-supervised regression

UR - http://www.scopus.com/inward/record.url?scp=85079327596&partnerID=8YFLogxK

U2 - 10.7712/120219.6338.18377

DO - 10.7712/120219.6338.18377

M3 - Conference contribution

AN - SCOPUS:85079327596

SN - 9786188284494

T3 - Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019

SP - 229

EP - 242

BT - Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019

A2 - Papadrakakis, M.

A2 - Papadopoulos, V.

A2 - Stefanou, G.

PB - National Technical University of Athens

T2 - 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019

Y2 - 24 June 2019 through 26 June 2019

ER -

ID: 25504709