2006
DOI: 10.1109/tit.2006.885449
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The Distributed Karhunen–Loève Transform

Abstract: Abstract-The Karhunen-Loève transform (KLT) is a key element of many signal processing and communication tasks. Many recent applications involve distributed signal processing, where it is not generally possible to apply the KLT to the entire signal; rather, the KLT must be approximated in a distributed fashion. This paper investigates such distributed approaches to the KLT, where several distributed terminals observe disjoint subsets of a random vector.We introduce several versions of the distributed KLT. Firs… Show more

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Cited by 180 publications
(233 citation statements)
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References 42 publications
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“…They focus on saving part of the multi-hop communication cost by either local computations (Kargupta, Huang, Sivakumar, & Johnson, 2001) or aggregation services (Bai, Chan, & Luk, 2005;Le Borgne, Raybaud, & Bontempi, 2008;Qi & Wang, 2004), but they still rely on a fusion center for merging the local results. In the context of distributed compression and source coding, Gastpar, Dragotti, and Vetterli (2006) proposed a distributed Karhunen-Loéve transform which is posed as an optimization problem, where convergence to the global optimum is, in general, not assured. Our two consensus-based distributed PCA algorithms (Valcarcel Macua, Belanovic, & Zazo, 2010) outperform all the above because they both guarantee convergence, with no fusion center, just by local neighborhood communications.…”
Section: Related Workmentioning
confidence: 99%
“…They focus on saving part of the multi-hop communication cost by either local computations (Kargupta, Huang, Sivakumar, & Johnson, 2001) or aggregation services (Bai, Chan, & Luk, 2005;Le Borgne, Raybaud, & Bontempi, 2008;Qi & Wang, 2004), but they still rely on a fusion center for merging the local results. In the context of distributed compression and source coding, Gastpar, Dragotti, and Vetterli (2006) proposed a distributed Karhunen-Loéve transform which is posed as an optimization problem, where convergence to the global optimum is, in general, not assured. Our two consensus-based distributed PCA algorithms (Valcarcel Macua, Belanovic, & Zazo, 2010) outperform all the above because they both guarantee convergence, with no fusion center, just by local neighborhood communications.…”
Section: Related Workmentioning
confidence: 99%
“…The optimal orthogonal transform for source separation of a Gaussian vector scale mixture is KLT, when the contrast function is the divergence-based cost of (20).…”
Section: Source Separation Problemmentioning
confidence: 99%
“…A number of distributed coding algorithms have been developed that involve collaboration amongst sensors [5,6]. Any collaboration, however, involves inter-sensor communication overhead that can significantly affect the power consumption of the participating nodes.…”
Section: Distributed Source Codingmentioning
confidence: 99%