We consider two Riemannian geometries for the manifold M(p,m×n)\documentclass[12pt]{minimal}
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\begin{document}$${\mathcal{M }(p,m\times n)}$$\end{document} of all m×n\documentclass[12pt]{minimal}
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\begin{document}$$m\times n$$\end{document} matrices of rank p\documentclass[12pt]{minimal}
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\begin{document}$$p$$\end{document}. The geometries are induced on M(p,m×n)\documentclass[12pt]{minimal}
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\begin{document}$${\mathcal{M }(p,m\times n)}$$\end{document} by viewing it as the base manifold of the submersion π:(M,N)↦MNT\documentclass[12pt]{minimal}
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\begin{document}$$\pi :(M,N)\mapsto MN^\mathrm{T}$$\end{document}, selecting an adequate Riemannian metric on the total space, and turning π\documentclass[12pt]{minimal}
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\begin{document}$$\pi $$\end{document} into a Riemannian submersion. The theory of Riemannian submersions, an important tool in Riemannian geometry, makes it possible to obtain expressions for fundamental geometric objects on M(p,m×n)\documentclass[12pt]{minimal}
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\begin{document}$${\mathcal{M }(p,m\times n)}$$\end{document} and to formulate the Riemannian Newton methods on M(p,m×n)\documentclass[12pt]{minimal}
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\begin{document}$${\mathcal{M }(p,m\times n)}$$\end{document} induced by these two geometries. The Riemannian Newton methods admit a stronger and more streamlined convergence analysis than the Euclidean counterpart, and the computational overhead due to the Riemannian geometric machinery is shown to be mild. Potential applications include low-rank matrix completion and other low-rank matrix approximation problems.