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Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering Научная публикация

Журнал Pattern Recognition and Image Analysis
ISSN: 1054-6618 , E-ISSN: 1555-6212
Вых. Данные Год: 2025, Том: 35, Номер: 1, Страницы: 16-18 Страниц : 3 DOI: 10.1134/S1054661824701414
Ключевые слова Weakly supervised regression, Manifold regularization, Co-association matrix, Fuzzy clustering, Cluster ensemble
Авторы Kalmutskiy Kirill 1,2 , Berikov Vladimir 2
Организации
1 Novosibirsk State University
2 Sobolev Institute of mathematics

Информация о финансировании (1)

1 Российский научный фонд 24-21-00195

Реферат: Weakly supervised learning algorithms have become increasingly important for modeling complex systems where precise labels are scarce or expensive to obtain. There are specialized algorithms for solving the weakly supervised regression problem, such as the Weakly Supervised Regression algorithm [1], which is based on manifold regularization and cluster ensemble. In this article, we introduce novel improvements to original algorithm, that significantly increase the stability and quality of the algorithm and reduce its dependence on properly selected hyperparameters. This result is achieved through the use of fuzzy clustering and consistency weights when constructing a cluster ensemble.
Библиографическая ссылка: Kalmutskiy K. , Berikov V.
Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering
Pattern Recognition and Image Analysis. 2025. V.35. N1. P.16-18. DOI: 10.1134/S1054661824701414 WOS Scopus РИНЦ OpenAlex
Даты:
Поступила в редакцию: 18 нояб. 2024 г.
Принята к публикации: 13 дек. 2024 г.
Опубликована в печати: 26 мар. 2025 г.
Опубликована online: 6 апр. 2025 г.
Идентификаторы БД:
Web of science: WOS:001460047000003
Scopus: 2-s2.0-105005069405
РИНЦ: 80615950
OpenAlex: W4409197418
Цитирование в БД: Пока нет цитирований
Альметрики: