This repository contains code of manifold embedded knowledge transfer (MEKT). MEKT is a novel transfer learning framework for offline unsupervised cross-subject electroencephalogram (EEG) classification. It can cope with variations among different individuals and/or tasks in unsupervised scenarios, and has...
Manifold Embedded Knowledge Transfer for Brain-Computer Interfacesdoi:10.1109/TNSRE.2020.2985996Wen ZhangDongrui WuIEEEInternational Conference of the IEEE Engineering in Medicine and Biology Society
VisualDomainAdaptationwithManifoldEmbeddedDistributionAlignment∗JindongWang,WenjieFeng,YiqiangChen†InstituteofComputingTechnology,CAS,B..
As differentiable manifolds, embedded in ℝN as well as abstract, became better understood, in particular under the influence of H. Whitney, it was not difficult to obtain a C∞-structure, unique but for equivalence on any C1-manifold: C∞↔C1is bijective. Manifolds being “slippery” ...
These interactions should be reflected in how TF activity correlates, even if these interactions are not explicitly embedded in the model. We select several typical examples of TF pairs with known interactions and compare inferred TFA between hierarchical SupirFactor and the Inferelator (Fig. 3C). ...
Qualitatively, the solution manifold .M covers too many independent directions to be embedded in a low-dimensional subspace. To address this issue, several techniques have been developed: • Problem-specific methods tackle the difficulties of some specific physics problems that are known to be non...
As BIF is an intrinsically low dimensional feature embedded in a high dimensional space, it is not suitable to measure the similarity between two BIFs directly based on the Euclidean distance. Therefore, it is necessary to obtain a suitable mapping to reduce the dimensionality. In this paper, ...
Similarly, the manifold embedded knowledge transfer (MEKT) framework [33] first whitened the SPD matrices of cross-subjects to an identity matrix, and then performed domain adaptation using tangent vectors to minimize the joint probability distribution shift between the source and the target domains,...
For good embedding, two similar data points should be as compact as possible in the low-dimensional subspace; if they are embedded far apart, a larger "penalty" should be imposed. Therefore, in the graph embedding model, an intrinsic graph and a penalty graph are constructed to represent ...
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 18–22 June 2023; pp. 7746–7755. [Google Scholar] Disclaimer/Publisher’s Note: The statements, ...