TY - GEN

T1 - On a Weakly Supervised Classification Problem

AU - Berikov, Vladimir

AU - Litvinenko, Alexander

AU - Pestunov, Igor

AU - Sinyavskiy, Yuriy

N1 - Funding Information: The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no FWNF-2022-0015). The work was partly supported by RFBR grant 19-29-01175. A. Litvinenko was supported by funding from the Alexander von Humboldt Foundation. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - We consider a weakly supervised classification problem. It is a classification problem where the target variable can be unknown or uncertain for some subset of samples. This problem appears when the labeling is impossible, time-consuming, or expensive. Noisy measurements and lack of data may prevent accurate labeling. Our task is to build an optimal classification function. For this, we construct and minimize a specific objective function, which includes the fitting error on labeled data and a smoothness term. Next, we use covariance and radial basis functions to define the degree of similarity between points. The further process involves the repeated solution of an extensive linear system with the graph Laplacian operator. To speed up this solution process, we introduce low-rank approximation techniques. We call the resulting algorithm WSC-LR. Then we use the WSC-LR algorithm for analysis CT brain scans to recognize ischemic stroke disease. We also compare WSC-LR with other well-known machine learning algorithms.

AB - We consider a weakly supervised classification problem. It is a classification problem where the target variable can be unknown or uncertain for some subset of samples. This problem appears when the labeling is impossible, time-consuming, or expensive. Noisy measurements and lack of data may prevent accurate labeling. Our task is to build an optimal classification function. For this, we construct and minimize a specific objective function, which includes the fitting error on labeled data and a smoothness term. Next, we use covariance and radial basis functions to define the degree of similarity between points. The further process involves the repeated solution of an extensive linear system with the graph Laplacian operator. To speed up this solution process, we introduce low-rank approximation techniques. We call the resulting algorithm WSC-LR. Then we use the WSC-LR algorithm for analysis CT brain scans to recognize ischemic stroke disease. We also compare WSC-LR with other well-known machine learning algorithms.

KW - Computed tomography

KW - Low-rank approximation

KW - Manifold regularization

KW - Similarity matrix

KW - Uncertainty model

KW - Weakly supervised classification

UR - http://www.scopus.com/inward/record.url?scp=85142694919&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/0dba924f-5e01-35ac-b2e5-88f6456bcce2/

U2 - 10.1007/978-3-031-16500-9_26

DO - 10.1007/978-3-031-16500-9_26

M3 - Conference contribution

AN - SCOPUS:85142694919

SN - 9783031164996

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 315

EP - 329

BT - Analysis of Images, Social Networks and Texts - 10th International Conference, AIST 2021, Revised Selected Papers

A2 - Burnaev, Evgeny

A2 - Ivanov, Sergei

A2 - Panchenko, Alexander

A2 - Ignatov, Dmitry I.

A2 - Kuznetsov, Sergei O.

A2 - Khachay, Michael

A2 - Koltsova, Olessia

A2 - Kutuzov, Andrei

A2 - Loukachevitch, Natalia

A2 - Napoli, Amedeo

A2 - Pardalos, Panos M.

A2 - Saramäki, Jari

A2 - Savchenko, Andrey V.

A2 - Tsymbalov, Evgenii

A2 - Tutubalina, Elena

PB - Springer Science and Business Media Deutschland GmbH

T2 - 10th International Conference on Analysis of Images, Social Networks and Texts, AIST 2021

Y2 - 16 December 2021 through 18 December 2021

ER -