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Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-08
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science.
This paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal.
The algorithm first compresses the potential exemplar list and reduces the pairwise similarity matrix.
By processing the most complete data in the first stage, FEEC then extends the few incomplete data into the exemplar list.
A new compressed similarity matrix will be constructed and the scale of this matrix is greatly reduced.
Finally, FEEC optimizes the new target function by the enhanced α-expansion move method.
On the other hand, due to the pairwise relationship, FEEC also improves the generalization of this algorithm.
In contrast to other exemplar-based models, the performance of the proposed clustering algorithm is comprehensively verified by the experiments on two datasets.
American Psychological Association (APA)
Bi, Anqi& Ying, Wenhao& Zhao, Lu. 2020. Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139428
Modern Language Association (MLA)
Bi, Anqi…[et al.]. Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139428
American Medical Association (AMA)
Bi, Anqi& Ying, Wenhao& Zhao, Lu. Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139428
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139428