We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
d abstract] Robert Krauthgamer ∗ IBM Almaden James R. Lee † Institute for Advanced Study We initiate the study of approximate algorithms on negatively curved spaces. These spa...
—We consider the problem of positioning a cloud of points in the Euclidean space Rd , from noisy measurements of a subset of pairwise distances. This task has applications in var...
By introducing redundant Klee-Minty examples, we have previously shown that the central path can be bent along the edges of the Klee-Minty cubes, thus having 2n - 2 sharp turns in...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...