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Sparse feature learning for multi-class Parkinson's disease classification.
| Content Provider | Europe PMC |
|---|---|
| Author | Lei, Haijun Zhao, Yujia Wen, Yuting Luo, Qiuming Cai, Ye Liu, Gang Lei, Baiying |
| Editor | Gómez, Carlos Schwarzacher, Severin P. Zhou, Huiyu |
| Copyright Year | 2018 |
| Abstract | This paper solves the multi-class classification problem for Parkinson’s disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher’s linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance. Differing in previous methods for binary classification, we perform a multi-class classification for PD diagnosis. Our proposed method is evaluated on the public available Parkinson’s progression markers initiative (PPMI) datasets. Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6004973&blobtype=pdf |
| ISSN | 09287329 |
| Journal | Technology and Health Care [Technol Health Care] |
| Volume Number | 26 |
| PubMed Central reference number | PMC6004973 |
| Issue Number | Suppl 1 |
| Issue Number | s1 |
| PubMed reference number | 29710748 |
| e-ISSN | 18787401 |
| DOI | 10.3233/thc-174548 |
| Language | English |
| Publisher | IOS Press |
| Publisher Date | 2018-01-01 |
| Publisher Place | Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands |
| Access Restriction | Open |
| Rights License | This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0). © 2018 – IOS Press and the authors. All rights reserved |
| Subject Keyword | Parkinson’s disease multi-class feature selection classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Biomaterials Biophysics Information Systems Bioengineering Biomedical Engineering Health Informatics |
