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

Standard

Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering. / Kalmutskiy, K.; Berikov, V.

In: Pattern Recognition and Image Analysis, Vol. 35, No. 1, 06.04.2025, p. 16-18.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{8976d82d7d0349e6b248ba73248741bc,

title = "Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering",

abstract = "Abstract: 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.",

keywords = "cluster ensemble, co-association matrix, fuzzy clustering, manifold regularization, weakly supervised regression",

author = "K. Kalmutskiy and V. Berikov",

note = "This work was supported by the Russian Science Foundation (grant no. 24-21-00195). Kalmutskiy, K. Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering / K. Kalmutskiy, V. Berikov // Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications. – 2025. – Vol. 35, No. 1. – P. 16-18. – DOI 10.1134/S1054661824701414.",

year = "2025",

month = apr,

day = "6",

doi = "10.1134/S1054661824701414",

language = "English",

volume = "35",

pages = "16--18",

journal = "Pattern Recognition and Image Analysis",

issn = "1054-6618",

publisher = "ФГБУ {"}Издательство {"}Наука{"}",

number = "1",

}

RIS

TY - JOUR

T1 - Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering

AU - Kalmutskiy, K.

AU - Berikov, V.

N1 - This work was supported by the Russian Science Foundation (grant no. 24-21-00195). Kalmutskiy, K. Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering / K. Kalmutskiy, V. Berikov // Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications. – 2025. – Vol. 35, No. 1. – P. 16-18. – DOI 10.1134/S1054661824701414.

PY - 2025/4/6

Y1 - 2025/4/6

N2 - Abstract: 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.

AB - Abstract: 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.

KW - cluster ensemble

KW - co-association matrix

KW - fuzzy clustering

KW - manifold regularization

KW - weakly supervised regression

UR - https://www.mendeley.com/catalogue/954c3823-239f-3b28-86a5-ec21edb1b919/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-105005069405&origin=inward&txGid=b2c679f65eb6535ac89bb9d5b97d85be

UR - https://www.elibrary.ru/item.asp?id=80615950

U2 - 10.1134/S1054661824701414

DO - 10.1134/S1054661824701414

M3 - Article

VL - 35

SP - 16

EP - 18

JO - Pattern Recognition and Image Analysis

JF - Pattern Recognition and Image Analysis

SN - 1054-6618

IS - 1

ER -

ID: 66683777