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In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the development of numerous algorithms of varying degre...
Given a class of closed Riemannian manifolds with prescribed geometric conditions, we introduce an embedding of the manifolds into $\ell^2$ based on the heat kernel of the Connection Laplacian associa...
Riemannian statistics geometry is proposed in this work as a counterpart approach of inference geometry.
In partially linear models the dependence of the response y on (xt, t) is modeled through the relationship y = xt +g(t)+" where " is independent of (xt, t). In this paper, estimators of g are constr...
Let γ be a smooth compact oriented manifold without boundary, imbedded in a euclidean space Es, and let γ be a smooth map of  into a Riemannian manifold . An unknown state θ 2  is observed via X...

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