The Fisher kernel (4b), on the other hand, respects the structure of the parameters in their original Riemannian manifold by working in the gradient space ... the Fisher kernel outperformed the other ...

in collaboration with RIS phase shift and statistical transmit covariance matrix optimization, while minimizing the PI power. As the proposed optimization problem is non-convex, we tailor a ...

Nature Research Intelligence Topics enable transformational understanding and discovery in research by categorising any document into meaningful, accessible topics. Read this blog to understand ...

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in ...

This is an extensive and continuously updated compilation of self-supervised GFM literature categorized by the knowledge-based taxonomy, proposed by our paper 📄A Survey on Self-Supervised Graph ...

The cvxcovariance package provides simple tools for creating an estimate $\hat\Sigma_t$ of the covariance $\Sigma_t$ of the $n$-dimensional return vectors $r_t$, $t=1 ...

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders.