Abstract: 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.

Cite: Berikov V. , Litvinenko A. , Pestunov I. , Sinyavskiy Y.
On a weakly supervised classification problem
Lecture Notes in Computer Science. 2022. V.13217. P.315–329. DOI: 10.1007/978-3-031-16500-9_26 Scopus OpenAlex

Identifiers:

Scopus:	2-s2.0-85142694919
OpenAlex:	W4312848287

Citing:

DB	Citing
Scopus	1

Altmetrics: