首页|Studies from Jaume I University Add New Findings in the Area of Machine Learning (A Gaussian Kernel for Kendall’s Space of m-d Shapes)
Studies from Jaume I University Add New Findings in the Area of Machine Learning (A Gaussian Kernel for Kendall’s Space of m-d Shapes)
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NETL
NSTL
Elsevier
Data detailed on Machine Learning have been presented. According to news reporting from Castellon de la Plana, Spain, by NewsRx journalists, research stated, “In this paper, we develop an approach to exploit kernel methods with data lying on the m-D Kendall shape space. When data arise in a finite-dimensional curved Riemannian manifold, as in this case, the usual Euclidean computer vision and machine learning algorithms must be treated carefully.” Financial supporters for this research include Univer-sitat Jaume I, Spain, Spanish Government, Spanish Government. The news correspondents obtained a quote from the research from Jaume I University, “A good approach is to use positive definite kernels on manifolds to embed the manifold with its corresponding metric in a high-dimensional reproducing kernel Hilbert space, where it is possible to utilize algorithms developed for linear spaces. Different Gaussian kernels can be found in the literature on the 2-D Kendall shape space to perform this embedding. The main novelty of this work is to provide a Gaussian kernel for the m-D Kendall shape space. This new Kernel coincides in the case m = 2 with the Gaussian kernels most widely used in the Kendall planar shape space and allows to define an embedding of the m-D Kendall shape space into a reproducible kernel Hilbert space. As far as we know, the complexity of the m-D Kendall shape space has meant that this embedding has not been addressed in the literature until now.”
Castellon de la PlanaSpainEuropeCyborgsEmerging TechnologiesMachine LearningJaume I University
NETL
NSTL
Elsevier
