Name
Title | ||
|---|---|---|
Missing-data Classification with the Extended Full-dimensional Gaussian Mixture Model: Applications to EMG-based Motion Recognition |
Abstract | ||
|---|---|---|
Missing data is a common drawback that pattern recognition techniques need to handle when solving reallife classification tasks. This paper first discusses problems in handling high-dimensional samples with missing values by the Gaussian mixture model (GMM). Since fitting the GMM by directly using high-dimensional samples as inputs is difficult due to the convergence and stability issues, a novel method is proposed to build the high-dimensional GMM by extending a reduced-dimensional GMM to the full-dimensional space. Based on the extended full-dimensional GMM, two approaches, namely, marginalization and conditional-mean imputation, are proposed to classify samples with missing-data in online phase. Then, the proposed methods were employed to recognize hand motions from surface electromyography (sEMG) signals, and more than 75% of classification accuracy of motions can be obtained even if 50% of sEMG signals were missing. Comparisons with normal mean and zero imputations also demonstrate the improvements of the proposed methods. Finally, a control scheme for a myoelectric hand was designed by involving the novel methods, and online experiments confirm the ability of the proposed methods to improve the safety and stability of practical systems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIE.2015.2403797 | Industrial Electronics, IEEE Transactions |
Keywords | Field | DocType |
classification,gaussian mixture model (gmm),electromyography,missing data,myoelectric hand,data models,feature extraction,gaussian mixture model,vectors | Convergence (routing),Data modeling,Pattern recognition,Motion recognition,Feature extraction,Artificial intelligence,Missing data,Imputation (statistics),Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
PP | 99 | 0278-0046 |
Citations | PageRank | References |
11 | 0.70 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qichuan Ding | 1 | 11 | 1.71 |
| Jianda Han | 2 | 220 | 60.61 |
| Xingang Zhao | 3 | 99 | 16.52 |
| Yang Chen | 4 | 12 | 2.06 |