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Sampling algorithms for weighted networks
Social Network Analysis and Mining, 2016Many of the real-world networks, such as complex social networks, are intrinsically weighted networks, and therefore, traditional network models, such as binary network models, will result in losing much of the information contained in the edge weights of the networks and is not very realistic.
Alireza Rezvanian, Mohammad Reza Meybodi
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Universal algorithm for compressive sampling
2015 23rd European Signal Processing Conference (EUSIPCO), 2015In a standard compressive sampling (CS) setup, we develop a universal algorithm where multiple CS reconstruction algorithms participate and their outputs are fused to achieve a better reconstruction performance. The new method is called universal algorithm for CS (UACS) that is iterative in nature and has a restricted isometry property (RIP) based ...
Ahmed Zaki +2 more
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An Exact Algorithm for Maximum Entropy Sampling [PDF]
Operations Research, 1995 We study the experimental design problem of selecting a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology.
KO, Chun-Wa +2 more
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Coprime sampling and the music algorithm
2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2011A new approach to super resolution line spectrum estimation in both temporal and spatial domain using a coprime pair of samplers is proposed. Two uniform samplers with sample spacings MT and NT are used where M and N are coprime and T has the dimension of space or time.
Pal, Piya, Vaidyanathan, P. P.
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Analysis of Regression Algorithms with Unbounded Sampling
Neural Computation, 2020In this letter, we study a class of the regularized regression algorithms when the sampling process is unbounded. By choosing different loss functions, the learning algorithms can include a wide range of commonly used algorithms for regression. Unlike the prior work on theoretical analysis of unbounded sampling, no constraint on the output variables ...
Hongzhi Tong, Jiajing Gao
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A Family of Unsupervised Sampling Algorithms
2019Three algorithms for unsupervised sampling are introduced. They are easy to tune, scalable, and yield a small size sample. They are based on the same concepts: they combine density and distance, they use the farthest-first traversal that allows for runtime optimization, they yield a coreset, and they are driven by a single user parameter.
Guillaume, S., Ros, F.
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Evolutionary Algorithms in the Presence of Noise: To Sample or Not to Sample
2007 IEEE Symposium on Foundations of Computational Intelligence, 2007In this paper, we empirically analyze the convergence behavior of evolutionary algorithms (evolution strategies - ES and genetic algorithms A) for two noisy optimization problems which belong to the class of functions with noise induced multi-modality (FNIMs).
Hans-Georg Beyer, Bernhard Sendhoff
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Simple algorithm for sampling synchronization of ADCs
2008 Conference on Precision Electromagnetic Measurements Digest, 2008This paper describes an algorithm for synchronizing analog-to-digital converters (ADCs) to the fundamental of a continuous periodic signal. It operates like a digital phase-locked loop, is based mainly on discrete Fourier transform (DFT) on sampled data, and uses DFT-leakage detection to ensure synchronous sampling.
Waldemar G. Kürten Ihlenfeld +1 more
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An Early Traffic Sampling Algorithm
2014The first several packets of a flow play key role in the on-line traffic managements. Early traffic sampling, extracting the first several packets of every flow, is raised. This paper proposes a structure named CTBF, combination of counting Bloom Filter and time Bloom Filter.
Ying Hou +4 more
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Algorithms for the Sample Mean of Graphs
2009Measures of central tendency for graphs are important for protoype construction, frequent substructure mining, and multiple alignment of protein structures. This contribution proposes subgradient-based methods for determining a sample mean of graphs. We assess the performance of the proposed algorithms in a comparative empirical study.
Brijnesh J. Jain, Klaus Obermayer
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