Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric | Sciact - Система учета научной деятельности ИМ СО РАН

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

Конференция

22nd International conference "Mathematical Optimization Theory and Operations Research"
02-08 июл. 2023 , Екатеринбург

Сборник

Mathematical Optimization Theory and Operations Research: Recent Trends
Сборник, Springer. 2023. 406 c.

Журнал

Communications in Computer and Information Science
ISSN: 1865-0929

Вых. Данные

Год: 2023, Том: 1881, Страницы: 364-375 Страниц : 12 DOI: 10.1007/978-3-031-43257-6_27

Ключевые слова

Cluster ensemble, Co-association matrix, Low-rank matrix representation, Manifold regularization, Multi-target regression, Weakly supervised learning

Авторы

Kalmutskiy Kirill ^1,2 , Cherikbayeva Lyailya ³ , Litvinenko Alexander ⁴ , Berikov Vladimir ^1,2

Организации

1	Novosibirsk State University, Novosibirsk, Russia
2	Sobolev Institute of mathematics, Novosibirsk, Russia
3	Al-Farabi Kazakh National University, Almaty, Kazakhstan
4	RWTH Aachen, Aachen, Germany

Информация о финансировании (1)

Российский научный фонд

22-21-00261

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.

Kalmutskiy K. , Cherikbayeva L. , Litvinenko A. , Berikov V.
Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric
В сборнике 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

Опубликована в печати:	21 сент. 2023 г.
Опубликована online:	21 сент. 2023 г.

Scopus:	2-s2.0-85174577438
OpenAlex:	W4386891740

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