Estimating characteristics of large graphs via sampling is a vital part of the study of complex networks. Current sampling methods such as (independent) random vertex and random w...
Recently, it was shown that it is possible to sample classes of 1D and 2-D signals with finite rate of innovation (FRI) [9, 4, 5, 3, 2, 7]. In particular, in [7], we presented loc...
Abstract—Many practical applications require the reconstruction of images from irregularly sampled data. The spline formalism offers an attractive framework for solving this prob...
Oleksii Vyacheslav Morozov, Michael Unser, Patrick...
Efficient index construction in multidimensional data spaces is important for many knowledge discovery algorithms, because construction times typically must be amortized by perform...
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...