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Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric. / Kalmutskiy, Kirill; Cherikbayeva, Lyailya; Litvinenko, Alexander et al.

Communications in Computer and Information Science. Springer Science and Business Media Deutschland GmbH, 2023. p. 364-375 (Communications in Computer and Information Science; Vol. 1881 CCIS).

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

Harvard

Kalmutskiy, K, Cherikbayeva, L, Litvinenko, A & Berikov, V 2023, Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric. in Communications in Computer and Information Science. Communications in Computer and Information Science, vol. 1881 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 364-375. https://doi.org/10.1007/978-3-031-43257-6_27

APA

Kalmutskiy, K., Cherikbayeva, L., Litvinenko, A., & Berikov, V. (2023). Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric. In Communications in Computer and Information Science (pp. 364-375). (Communications in Computer and Information Science; Vol. 1881 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43257-6_27

Vancouver

Kalmutskiy K, Cherikbayeva L, Litvinenko A, Berikov V. Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric. In Communications in Computer and Information Science. Springer Science and Business Media Deutschland GmbH. 2023. p. 364-375. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-43257-6_27

Author

Kalmutskiy, Kirill ; Cherikbayeva, Lyailya ; Litvinenko, Alexander et al. / Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric. Communications in Computer and Information Science. Springer Science and Business Media Deutschland GmbH, 2023. pp. 364-375 (Communications in Computer and Information Science).

BibTeX

@inproceedings{b99cd93d4ddd45b5b3640a0d4406f288,
title = "Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric",
abstract = "In this paper, we consider the weakly supervised multi-target regression problem where the observed data is partially or imprecisely labelled. The model of the multivariate normal distribution over the target vectors represents the uncertainty arising from the labelling process. The proposed solution is based on the combination of a manifold regularisation method, the use of the Wasserstein distance between multivariate distributions, and a cluster ensemble technique. The method uses a low-rank representation of the similarity matrix. An algorithm for constructing a co-association matrix with calculation of the optimal number of clusters in a partition is presented. To increase the stability and quality of the ensemble clustering, we use k-means with different distance metrics. The experimental part presents the results of numerical experiments with the proposed method on artificially generated data and real data sets. The results show the advantages of the proposed method over existing solutions.",
keywords = "Cluster ensemble, Co-association matrix, Low-rank matrix representation, Manifold regularization, Multi-target regression, Weakly supervised learning",
author = "Kirill Kalmutskiy and Lyailya Cherikbayeva and Alexander Litvinenko and Vladimir Berikov",
note = "The work was carried out with the financial support of the Russian Science Foundation, project 22-21-00261. Special thanks to Vladimir Kondratiev for participating in the discussion and experiments. Публикация для корректировки.",
year = "2023",
doi = "10.1007/978-3-031-43257-6_27",
language = "English",
isbn = "9783031432569",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "364--375",
booktitle = "Communications in Computer and Information Science",
address = "Germany",

}

RIS

TY - GEN

T1 - Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric

AU - Kalmutskiy, Kirill

AU - Cherikbayeva, Lyailya

AU - Litvinenko, Alexander

AU - Berikov, Vladimir

N1 - The work was carried out with the financial support of the Russian Science Foundation, project 22-21-00261. Special thanks to Vladimir Kondratiev for participating in the discussion and experiments. Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - In this paper, we consider the weakly supervised multi-target regression problem where the observed data is partially or imprecisely labelled. The model of the multivariate normal distribution over the target vectors represents the uncertainty arising from the labelling process. The proposed solution is based on the combination of a manifold regularisation method, the use of the Wasserstein distance between multivariate distributions, and a cluster ensemble technique. The method uses a low-rank representation of the similarity matrix. An algorithm for constructing a co-association matrix with calculation of the optimal number of clusters in a partition is presented. To increase the stability and quality of the ensemble clustering, we use k-means with different distance metrics. The experimental part presents the results of numerical experiments with the proposed method on artificially generated data and real data sets. The results show the advantages of the proposed method over existing solutions.

AB - In this paper, we consider the weakly supervised multi-target regression problem where the observed data is partially or imprecisely labelled. The model of the multivariate normal distribution over the target vectors represents the uncertainty arising from the labelling process. The proposed solution is based on the combination of a manifold regularisation method, the use of the Wasserstein distance between multivariate distributions, and a cluster ensemble technique. The method uses a low-rank representation of the similarity matrix. An algorithm for constructing a co-association matrix with calculation of the optimal number of clusters in a partition is presented. To increase the stability and quality of the ensemble clustering, we use k-means with different distance metrics. The experimental part presents the results of numerical experiments with the proposed method on artificially generated data and real data sets. The results show the advantages of the proposed method over existing solutions.

KW - Cluster ensemble

KW - Co-association matrix

KW - Low-rank matrix representation

KW - Manifold regularization

KW - Multi-target regression

KW - Weakly supervised learning

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85174577438&origin=inward&txGid=f9d3fd059404604c75f91fdd54b21d4e

UR - https://www.mendeley.com/catalogue/364c1269-d1aa-31c5-ae29-cc6de6aefdbe/

U2 - 10.1007/978-3-031-43257-6_27

DO - 10.1007/978-3-031-43257-6_27

M3 - Conference contribution

SN - 9783031432569

T3 - Communications in Computer and Information Science

SP - 364

EP - 375

BT - Communications in Computer and Information Science

PB - Springer Science and Business Media Deutschland GmbH

ER -

ID: 59183170