IRTUM – Institutional Repository of the Technical University of Moldova

Unsupervised Knowledge Extraction from Biomedical Data

Show simple item record

dc.contributor.author BASARAB, Matei
dc.contributor.author VLAICU, Petru Alexandru
dc.contributor.author ROGOVSCHI, Nicoleta
dc.contributor.author GROZAVU, Nistor
dc.date.accessioned 2023-11-14T12:11:36Z
dc.date.available 2023-11-14T12:11:36Z
dc.date.issued 2023
dc.identifier.citation BASARAB, Matei, VLAICU, Petru Alexandru, ROGOVSCHI, Nicoleta. Unsupervised Knowledge Extraction from Biomedical Data. In: 6th International Conference on Nanotechnologies and Biomedical Engineering: proc. of ICNBME-2023, 20–23, 2023, Chisinau, vol. 2: Biomedical Engineering and New Technologies for Diagnosis, Treatment, and Rehabilitation, 2023, p. 243-254. ISBN 978-3-031-42781-7. e-ISBN 978-3-031-42782-4. en_US
dc.identifier.isbn 978-3-031-42781-7
dc.identifier.isbn 978-3-031-42782-4
dc.identifier.uri https://doi.org/10.1007/978-3-031-42782-4_27
dc.identifier.uri http://repository.utm.md/handle/5014/24798
dc.description Acces full text - https://doi.org/10.1007/978-3-031-42782-4_27 en_US
dc.description.abstract In this paper we introduce a study on the use of the unsupervised representation learning on biomedical data i.e. on Growth weight data and Wisconsin Diagnostic Breast Cancer obtaining good performances in terms of clustering In this study, we propose an adaptation of the unsupervised topological learning to deals with biomedical datasets based on a new approximation strategy to visualize high dimensional datasets. In data containing high-dimensional data manifold, the level of the discrepancy changes depending on the dimension of intrinsic data manifold. Then the strength of the repelling power is dependent of dataset. The proposed approach is based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method with a different inhomogenous approximation strategy of the t-Distribution. In order to avoid the exponential computation we propose an inhomogenous approximation of the t-Distribution having the precision order of 10−3. By using this inhomogenous approximation we allow to optimize approximately the t-Distribution with respect to the number of degree of freedom and also to reduce the computational time. We illustrate the power of the proposed approach with two bio-medical real datasets and the obtained results outperform classical SNE and t-SNE methods. en_US
dc.language.iso en en_US
dc.publisher Springer Nature Switzerland en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject data visualization en_US
dc.subject dimensional reduction en_US
dc.subject clustering en_US
dc.title Unsupervised Knowledge Extraction from Biomedical Data en_US
dc.type Article en_US


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

  • 2023
    6th International Conference on Nanotechnologies and Biomedical Engineering, September 20–23, 2023, Chisinau, Moldova

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

Search DSpace


Browse

My Account