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
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 -