Performance evaluation of Dictionary Learning and ICA on Parkinson’s patients classification using Machine Learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16485-5.pdf
Reference29 articles.
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2. Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, Shen D (2016) Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data. Neuroimage. 141:206–219. https://doi.org/10.1016/j.neuroimage.2016.05.054
3. Adeli E, Thung K-H, An L, Wu G, Shi F, Wang T et al (2019) Semi-supervised discriminative classification robust to sample outliers and feature noises. IEEE Trans Pattern Anal Mach Intell 41:515–522. https://doi.org/10.1109/TPAMI.2018.2794470
4. Amirali K, Soroosh G, Hamid S (2017) Graph Theoretical Metrics and Machine Learning for Diagnosis of Parkinson's Disease Using rs-fMRI. Artif Intell Signal Process. https://doi.org/10.1109/AISP.2017.8324124
5. Archer DB, Bricker JT, Chu WT, Burciu RG (2019) Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study. The Lancet Digital Health 1(5):e222–e231
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