In this article, we present a rule-based language dedicated to topological operations, based on graph transformations. Generalized maps are described as a particular class of graph...
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensionality spaces, often revealing the true intrinsic dimensio...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...
In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid ...
Recent local state-of-the-art stereo algorithms based on variable cost aggregation strategies allow for inferring disparity maps comparable to those yielded by algorithms based on ...
Stefano Mattoccia, Simone Giardino, Andrea Gambini
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...