Name
Abstract | ||
|---|---|---|
In transfer learning scenarios, previous discriminative dimensionality reduction methods tend to perform poorly owing to the difference between source and target distributions. In such cases, it is unsuitable to only consider discrimination in the low-dimensional source latent space since this would generalize badly to target domains. In this paper, we propose a new dimensionality reduction method for transfer learning scenarios, which is called transferable discriminative dimensionality reduction (TDDR). By resolving an objective function that encourages the separation of the domain-merged data and penalizes the distance between source and target distributions, we can find a low-dimensional latent space which guarantees not only the discrimination of projected samples, but also the transferability to enable later classification or regression models constructed in the source domain to generalize well to the target domain. In the experiments, we firstly analyze the perspective of transfer learning in brain-computer interface (BCI) research and then test TDDR on two real datasets from BCI applications. The experimental results show that the TDDR method can learn a low-dimensional latent feature space where the source models can perform well in the target domain. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICTAI.2011.134 | ICTAI |
Keywords | Field | DocType |
target distributions,previous discriminative dimensionality reduction,low-dimensional latent space,new dimensionality reduction method,learning (artificial intelligence),regression analysis,brain-computer interfaces,fisher discriminant analysis,dimensionality reduction,source domain,target distribution,transfer learning,source distributions,target domain,tddr method,source model,regression models,transferable discriminative dimensionality reduction method,low-dimensional latent feature space,bci research,transferable discriminative dimensionality reduction,brain-computer interface,domain-merged data separation,low-dimensional source latent space,machine learning,brain computer interface,brain computer interfaces,objective function,regression model,feature space,learning artificial intelligence,principal component analysis | Feature vector,Dimensionality reduction,Pattern recognition,Computer science,Regression analysis,Transfer of learning,Brain–computer interface,Artificial intelligence,Discriminative model,Transferability,Principal component analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 E-ISBN : 978-0-7695-4596-7 | 978-0-7695-4596-7 | 6 |
PageRank | References | Authors |
0.49 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenting Tu | 1 | 85 | 9.48 |
| Shiliang Sun | 2 | 1732 | 115.55 |