We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more th...
In this paper a parallel implementation of a watershed algorithm is proposed. The algorithm is designed for a ring-architecture with distributed memory and a piece of shared memory...
One of the most fundamental problems in large-scale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely us...
The empirical mode decomposition (EMD) has seen widespread use for analysis of nonlinear and nonstationary time-series. Despite some practical success, it lacks a firm theoretica...
Stephen D. Hawley, Les E. Atlas, Howard J. Chizeck
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...