Self-repelling diffusions on a Riemannian manifold
Sign Up to like & getrecommendations! Published in 2017 at "Probability Theory and Related Fields"
DOI: 10.1007/s00440-016-0717-1
Abstract: Let M be a compact connected oriented Riemannian manifold. The purpose of this paper is to investigate the long time behavior of a degenerate stochastic differential equation on the state space $$M\times \mathbb {R}^{n}$$M×Rn; which… read more here.
Keywords: diffusions riemannian; self repelling; probability; riemannian manifold ... See more keywords
Every sub-Riemannian manifold is the Gromov–Hausdorff limit of a sequence Riemannian manifolds
Sign Up to like & getrecommendations! Published in 2017 at "Acta Mathematica Sinica, English Series"
DOI: 10.1007/s10114-017-4543-x
Abstract: In this paper, we will show that every sub-Riemannian manifold is the Gromov–Hausdorff limit of a sequence of Riemannian manifolds. read more here.
Keywords: gromov hausdorff; hausdorff limit; manifold gromov; every sub ... See more keywords
Four-dimensional pseudo-Riemannian g.o. spaces and manifolds
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Geometry and Physics"
DOI: 10.1016/j.geomphys.2018.03.018
Abstract: Abstract A g.o. manifold is a homogeneous pseudo-Riemannian manifold whose geodesics are all homogeneous, that is, they are orbits of a one-parameter group of isometries. A g.o. space is a realization of a homogeneous pseudo-Riemannian… read more here.
Keywords: pseudo; dimensional pseudo; riemannian spaces; four dimensional ... See more keywords
Stable spike clusters on a compact two-dimensional Riemannian manifold
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Differential Equations"
DOI: 10.1016/j.jde.2019.10.005
Abstract: Abstract We consider the Gierer-Meinhardt system with small inhibitor diffusivity and very small activator diffusivity on a compact two-dimensional Riemannian manifold without boundary. We study steady state solutions which are far from spatial homogeneity. We… read more here.
Keywords: spike clusters; two dimensional; compact two; riemannian manifold ... See more keywords
Using distance on the Riemannian manifold to compare representations in brain and in models
Sign Up to like & getrecommendations! Published in 2021 at "NeuroImage"
DOI: 10.1016/j.neuroimage.2021.118271
Abstract: Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation… read more here.
Keywords: riemannian manifold; distance; distance riemannian; brain models ... See more keywords
Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.
Sign Up to like & getrecommendations! Published in 2018 at "Journal of neural engineering"
DOI: 10.1088/1741-2552/aa8235
Abstract: OBJECTIVE In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. APPROACH A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines… read more here.
Keywords: speech imagery; eeg signals; speech; imagined speech ... See more keywords
Probability Distribution-Based Dimensionality Reduction on Riemannian Manifold of SPD Matrices
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3017234
Abstract: Representing images and videos with Symmetric Positive Definite (SPD) matrices and utilizing the intrinsic Riemannian geometry of the resulting manifold has proved successful in many computer vision tasks. Since SPD matrices lie in a nonlinear… read more here.
Keywords: spd matrices; probability distribution; spd; riemannian manifold ... See more keywords
RECLNet: Riemannian Manifold Enhanced Contrastive Learning Framework for PolSAR Image Few-Shot Classification
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2025.3545893
Abstract: In recent years, contrastive learning (CL) methods have achieved remarkable success in few-shot classification of polarimetric synthetic aperture radar (PolSAR) images. However, existing CL models based on Euclidean metric typically vectorize PolSAR data into real-valued… read more here.
Keywords: framework; riemannian manifold; shot classification; contrastive learning ... See more keywords
Enhanced Matrix CFAR Detection With Dimensionality Reduction of Riemannian Manifold
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3037489
Abstract: This letter proposes an enhanced matrix constant false alarm rate (CFAR) detection method that works on the lower-dimensional Riemannian manifold. Motivated by general matrix CFAR detection method and dimensionality reduction scheme of the Riemannian manifold,… read more here.
Keywords: cfar detection; dimensionality reduction; riemannian manifold; detection ... See more keywords
Constrained Riemannian Manifold Optimization for the Simultaneous Shaping of Ambiguity Function and Transmit Beampattern
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Aerospace and Electronic Systems"
DOI: 10.1109/taes.2024.3520951
Abstract: Designing the transmit waveforms with prescribed ambiguity functions (AFs) and beampatterns while adhering to the constant modulus (CM) constraint is pivotal for the forthcoming cognitive multiple-input multiple-output (MIMO) radar systems. This study delves into the… read more here.
Keywords: problem; riemannian manifold; constrained riemannian; transmit ... See more keywords
Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3190836
Abstract: Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance… read more here.
Keywords: feature space; based feature; riemannian manifold; image ... See more keywords