1. Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification
- Author
-
Tongguang Ni, Jing Xue, and Xiaoqing Gu
- Subjects
Computer science ,metric learning ,02 engineering and technology ,Electroencephalography ,lcsh:RC321-571 ,03 medical and health sciences ,0302 clinical medicine ,Discriminative model ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,medicine ,Representation (mathematics) ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Original Research ,EEG signal classification ,Quantitative Biology::Neurons and Cognition ,medicine.diagnostic_test ,business.industry ,General Neuroscience ,Pattern recognition ,multi-view learning ,Class (biology) ,auto-weight ,Constraint (information theory) ,Metric space ,ComputingMethodologies_PATTERNRECOGNITION ,Metric (mathematics) ,epilepsy ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Neuroscience - Abstract
Metric learning is a class of efficient algorithms for EEG signal classification problem. Usually, metric learning method deals with EEG signals in the single view space. To exploit the diversity and complementariness of different feature representations, a new auto-weighted multi-view discriminative metric learning method with Fisher discriminative and global structure constraints for epilepsy EEG signal classification called AMDML is proposed to promote the performance of EEG signal classification. On the one hand, AMDML exploits the multiple features of different views in the scheme of the multi-view feature representation. On the other hand, considering both the Fisher discriminative constraint and global structure constraint, AMDML learns the discriminative metric space, in which the intraclass EEG signals are compact and the interclass EEG signals are separable as much as possible. For better adjusting the weights of constraints and views, instead of manually adjusting, a closed form solution is proposed, which obtain the best values when achieving the optimal model. Experimental results on Bonn EEG dataset show AMDML achieves the satisfactory results.
- Published
- 2020