The problem of building an l 0-sampler is to sample near-uniformly from the support set of a dynamic multiset. This problem has a variety of applications within data analysis, computational geometry and graph algorithms. In this paper, we abstract a set of steps for building an l 0-sampler, based on sampling, recovery and selection. We analyze the implementation of an l 0-sampler within this framework, and show how prior constructions of l 0-samplers can all be expressed in terms of these steps. Our experimental contribution is to provide a first detailed study of the accuracy and computational cost of l 0-samplers. © 2013 Springer Science+Business Media New York.
Cormode, G., Firmani, D. (2014). A unifying framework for l0-sampling algorithms. DISTRIBUTED AND PARALLEL DATABASES, 32(3), 315-335 [10.1007/s10619-013-7131-9].
A unifying framework for l0-sampling algorithms
Firmani Donatella
2014-01-01
Abstract
The problem of building an l 0-sampler is to sample near-uniformly from the support set of a dynamic multiset. This problem has a variety of applications within data analysis, computational geometry and graph algorithms. In this paper, we abstract a set of steps for building an l 0-sampler, based on sampling, recovery and selection. We analyze the implementation of an l 0-sampler within this framework, and show how prior constructions of l 0-samplers can all be expressed in terms of these steps. Our experimental contribution is to provide a first detailed study of the accuracy and computational cost of l 0-samplers. © 2013 Springer Science+Business Media New York.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
