We give a provably correct algorithm to reconstruct a kdimensional manifold embedded in d-dimensional Euclidean space. Input to our algorithm is a point sample coming from an unkn...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
In this paper, we propose a novel learning-based method for image hallucination, with image super-resolution being a specific application that we focus on here. Given a low-resolu...
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al...
In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its highresolution counterpart using...