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This covariance matrix can be viewed as a manifold point in the space of SPD matrices.
In this case, we aim to partition the set of M manifold points into k (k≤M) subsets (or, clusters) (mathcal {C} = {mathcal {C}_{1}, mathcal {C}_{2}, cdots, mathcal {C}_{k}}) by minimizing the sum of squared geodesic distances of each manifold point in the cluster to its center.
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p and q are manifold points, i.e., (mathbf {p}, mathbf {q} in mathcal {S}^{n}).
P and Q are the manifold points, i.e., (mathbf {P}, mathbf {Q} in Sym_^{d}).
Mapping the former onto the latter is, for Schlick, an important part of the business of confirmation, but the reality of the spacetime manifold points is in no way consequent upon their observability.
where (mathcal {T}_{mathbf {P}}) is the tangent space at a manifold point P, (mathbf {Delta } in mathcal {T}_{mathbf {P}}) is the tangent vector whose projected point on the manifold is Q, exp is the matrix exponential, and log is the principal logarithm of a matrix defined as the inverse of the matrix exponential [12].
Between any two manifold points, there exists a unique shortest curve in that connects these two points.
In the tangent space of manifold points on a Riemannian manifold, linear operations may be performed.
It relates the real data in the chemical space with manifold points.
What Einstein realized in 1915 was that, in 1913, he was wrongly assuming that a coordinate chart sufficed to fix the identity of spacetime manifold points.
He distinguished macroscopic coincidences in the field of our sense experience, to which he does accord a privileged and foundational epistemic status, from the microscopic point coincidences that define an ontology of spacetime manifold points.
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CEO of Professional Science Editing for Scientists @ prosciediting.com