In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in highdimensional Euclidean spaces. We show that geodesic paths ...
We propose an algorithm for detecting bridges and estimating geodesic distances from a set of noisy samples of an underlying manifold. Finding geodesics on a nearest neighbors gra...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
Chinmay Hegde, Michael B. Wakin, Richard G. Barani...
Abstract. To develop statistical models for shapes, we utilize an elastic string representation where curves (denoting shapes) can bend and locally stretch (or compress) to optimal...
Anuj Srivastava, Aastha Jain, Shantanu H. Joshi, D...