Mar 10, 2017 · This paper successfully extends the framework of conscious competitive learning to the Riemannian manifold of the SPD matrices through operating ...
Using structured features such as symmetric positive definite (SPD) matrices to encode visual information has been found to be effective in computer vision.
Jan 15, 2021 · SPD(n) is a space of symmetric and positive definite matrices SPD ( n ) = { X ∈ R n × n | X = X ⊤ , λ min ( X ) > 0 } where λ m i n denotes ...
Using structured features such as symmetric positive definite (SPD) matrices to encode visual information has been found to be effective in computer vision.
Jul 1, 2019 · Recently, clustering in the Riemannian manifolds has received a great interest. The main specificity of this kind of spaces is their ability to ...
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Jun 26, 2023 · Bibliographic details on Clustering Symmetric Positive Definite Matrices on the Riemannian Manifolds.
In this paper, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), a kernel subspace clustering method is constructed un the SPD ...
Data representations based on Symmetric Positive Defi- nite (SPD) matrices are gaining popularity in visual learn- ing applications.
In this paper, we have introduced a family of provably positive definite kernels on the Riemannian manifold of. SPD matrices. We have shown that such kernels ...
In this paper, we present ap- plications to 2-D motion segmentation and diffusion tensor imaging segmentation that involve clustering on the space of symmetric ...