Name
Title | ||
|---|---|---|
Tensor-Variate Mixture Of Experts For Proportional Myographic Control Of A Robotic Hand |
Abstract | ||
|---|---|---|
When data are organized in matrices or arrays of higher dimensions (tensors), classical regression methods first transform these data into vectors, therefore ignoring the underlying structure of the data and increasing the dimensionality of the problem. This flattening operation typically leads to overfitting when only few training data is available. In this paper, we present a mixture-of-experts model that exploits tensorial representations for regression of tensor-valued data. The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available. Evaluation on artificially generated data, as well as offline and real-time experiments recognizing hand movements from tactile myography prove the effectiveness of the proposed approach. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.robot.2021.103812 | ROBOTICS AND AUTONOMOUS SYSTEMS |
Keywords | DocType | Volume |
Tensor methods, Mixture of experts, Generalized linear model, Tactile myography | Journal | 142 |
ISSN | Citations | PageRank |
0921-8890 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noémie Jaquier | 1 | 1 | 3.45 |
| Robert Haschke | 2 | 301 | 32.67 |
| Sylvain Calinon | 3 | 1897 | 117.63 |