Abstract--With the objective of minimizing the total cost, which includes both sensor and congestion costs, the authors adopted a novel sampling theorem approach to address the pro...
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...
We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
A useful theorem of Kuˇcera states that given a Martin-L¨of random infinite binary sequence ω and an effectively open set A of measure less than 1, some tail of ω is not in A...
Laurent Bienvenu, Adam R. Day, Ilya Mezhirov, Alex...
Spectral clustering has attracted much research interest in recent years since it can yield impressively good clustering results. Traditional spectral clustering algorithms first s...
Bo Chen, Bin Gao, Tie-Yan Liu, Yu-Fu Chen, Wei-Yin...