Results 281 to 290 of about 1,343,954 (317)

Semi-supervised geodesic Generative Topographic Mapping

Pattern Recognition Letters, 2010
We present a novel semi-supervised model, SS-Geo-GTM, which stems from a geodesic distance-based extension of Generative Topographic Mapping that prioritizes neighbourhood relationships along a generated manifold embedded in the observed data space. With this, it improves the trustworthiness and the continuity of the low-dimensional representations it ...
Raúl Cruz-Barbosa, Alfredo Vellido
exaly   +3 more sources

Sammon's nonlinear mapping using geodesic distances

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Li Yang
exaly   +3 more sources

On geodesic mappings of equidistant generalized Riemannian spaces

Applied Mathematics and Computation, 2012
The notion of \textit{generalized Riemannian space} was introduced by Eisenhart in 1951 as a manifold endowed with a non-symmetric metric tensor \(g=(g_{ij})\). Such a space is called \textit{equidistant} if there exists a \(1\)-form whose covariant derivative is proportional to the symmetric part of \(g\).
Mica S Stanković, Ljubica S Velimirović   +1 more
exaly   +2 more sources

Necessary and sufficient conditions for equitorsion geodesic mapping

Journal of Mathematical Analysis and Applications, 2016
Ljubica S Velimirović, Mica S Stanković   +1 more
exaly   +2 more sources

Almost Geodesic Mappings and Projections of the Sphere

Mathematical Notes, 2022
In the paper under review it is shown that the parallel and central projections of \(n\)-planes onto \(n\)-spheres, as well as the central projections of \(n\)-spheres from their centers onto \(n\)-spheres, are almost geodesic mappings. Examples of almost geodesics mappings of compact spaces are also constructed.
Mikeš, Josef, Guseva, Naděžda, Peška, Patrik, Rýparová, Lenka   +3 more
openaire   +3 more sources

Geodesic Mappings and Their Generalizations

Journal of Mathematical Sciences, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mikeš, Josef, Berezovski, Volodymyr E., Stepanova, E., Chudá, Hana   +3 more
openaire   +3 more sources

Geodesic Nonlinear Mapping Using the Neural Gas Network

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
Pablo A Estévez
exaly   +2 more sources

Geodesic self-organizing map

SPIE Proceedings, 2005
Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the “border effect”. In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce
Yingxin Wu 0001, Masahiro Takatsuka
openaire   +1 more source

Geodesic Basis Function Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2022
In the learning of existing radial basis function neural networks-based methods, it is difficult to propagate errors back. This leads to an inconsistency between the learning and recognition task.
Yang Zhao, Daokun Si, J. Pei, Xuan S. Yang   +3 more
semanticscholar   +1 more source

Optimization of Geodesic Self-Organizing Map

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
The Geodesic Self-Organizing Map (GeoSOM) is a variation of traditional SOM, which uses an icosahedron-based tessellation as spherical lattice to eliminate the border effect to minimize the distortion in the reduction of high-dimensional spaces. Border effect is a problem intrinsic of low-dimensional neural grid, where neurons in the border have a less
Romulo M. de Sousa, Roberto Célio Limão de Oliveira   +1 more
openaire   +1 more source