We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between ...
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...
To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding. New method First, the covariance matrices of the EEG trials are first aligned on the SPD manifold...
In the embedded space the manifold is described globally. Distributions on the manifold are also commonly defined in the embedded space in the form of the density function pH w.r.t. the Hausdorff measure on Ξ(M), such as the von Mises-Fisher distribution on hyperspheres. It is thus more...
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...
ManifoldEmbeddedDistributionAlignment (MEDA) method to address the challenges of both degenerated feature transfor- mation and unevaluated distribution alignment. MEDA learns a domain-invariant classi er in Grassmann manifold with struc- tural risk minimization, while performing dynamic distribution alignment ...
Therefore, it is assumed that some nonlinear manifolds of opinions are embedded into the high dimensional input space, and a few nearest neighbors can construct each data point in its manifold. The weights of this reconstruction for all data are learned by imposing the mentioned assumptions as pri...
In our approach, we take an intermediate position, whereby we assume that the training attribute space Y is embedded in a linear space, through Word2Vec trained on all English Wikipedia articles using the skip-gram neural network model [20]. We then focus on X and h, with the latter ...
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” ...
To polish the manifold without deformation, the assembled manifold is embedded in Crystalbond 509 mounting adhesive. The embedded manifold is hand polished using a succession of finer grit SiC abrasive papers. Crystalbond is dissolved in acetone following grinding. After removal, the manifold is soaked...