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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...
Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updat...
Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require ...
We first introduce a class of divergence measures between power spectral density matrices. These are derived by comparing the suitability of different models in the context of optimal prediction.
Riemannian statistics geometry is proposed in this work as a counterpart approach of inference geometry.
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belong in a Riemannian manifolds.
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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