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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Songhwai Oh |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Seoul, Korea (Songhwai Oh) |
| Abstract | The data association problem appears in many applications and is considered as the most challenging problem in intelligent systems. In this paper, we consider the Bayesian formulation of data association problems and present a deterministic polynomial-time approximation algorithm with guaranteed error bounds using correlation decay from statistical physics. We then show that the proposed algorithm naturally partitions a complex problem into a set of local problems and develop a distributed version of the algorithm. The performance of the proposed algorithm is evaluated in simulation. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 776461 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457705120 |
| e-ISBN | 9781457705137 |
| e-ISBN | 9781457705113 |
| DOI | 10.1109/DCOSS.2011.5982153 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Density measurement Distributed databases Approximation algorithms Polynomials Partitioning algorithms Approximation methods |
| Content Type | Text |
| Resource Type | Article |
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