Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric Full article
| Conference |
22nd International conference "Mathematical Optimization Theory and Operations Research" 02-08 Jul 2023 , Екатеринбург |
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| Source | Mathematical Optimization Theory and Operations Research: Recent Trends Compilation, Springer. 2023. 406 c. |
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| Journal |
Communications in Computer and Information Science
ISSN: 1865-0929 |
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| Output data | Year: 2023, Volume: 1881, Pages: 364-375 Pages count : 12 DOI: 10.1007/978-3-031-43257-6_27 | ||||||||
| Tags | Cluster ensemble, Co-association matrix, Low-rank matrix representation, Manifold regularization, Multi-target regression, Weakly supervised learning | ||||||||
| Authors |
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| Affiliations |
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Funding (1)
| 1 | Russian Science Foundation | 22-21-00261 |
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.
Cite:
Kalmutskiy K.
, Cherikbayeva L.
, Litvinenko A.
, Berikov V.
Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric
In compilation Mathematical Optimization Theory and Operations Research: Recent Trends. – Springer., 2023. – Т.1881. – C.364-375. DOI: 10.1007/978-3-031-43257-6_27 Scopus OpenAlex
Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric
In compilation Mathematical Optimization Theory and Operations Research: Recent Trends. – Springer., 2023. – Т.1881. – C.364-375. DOI: 10.1007/978-3-031-43257-6_27 Scopus OpenAlex
Dates:
| Published print: | Sep 21, 2023 |
| Published online: | Sep 21, 2023 |
Identifiers:
| Scopus: | 2-s2.0-85174577438 |
| OpenAlex: | W4386891740 |
Citing:
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