Results 1  10
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524
Clustering by passing messages between data points
 Science
, 2007
"... Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars ” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initi ..."
Abstract

Cited by 696 (8 self)
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if that initial choice is close to a good solution. We devised a method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Realvalued messages are exchanged between data points until a highquality set of exemplars and corresponding clusters gradually emerges
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
Abstract

Cited by 676 (15 self)
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in a more gen eral setting? We compare the marginals com puted using loopy propagation to the exact ones in four Bayesian network architectures, including two realworld networks: ALARM and QMR. We find that the loopy beliefs of ten converge and when they do, they give a good approximation
COMA  A system for flexible combination of Schema Matching Approaches
 In VLDB
, 2002
"... Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automati ..."
Abstract

Cited by 443 (12 self)
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 prehensively evaluate the effectiveness of different matchers and their combinations for realworld sche mas. The results obtained so far show the superiority of combined match approaches and indicate the high value of reuseoriented strategies.
AFFINITY PROPAGATION: CLUSTERING DATA BY PASSING MESSAGES
, 2009
"... Clustering data by identifying a subset of representative examples is important for detecting patterns in data and in processing sensory signals. Such “exemplars ” can be found by randomly choosing an initial subset of data points as exemplars and then iteratively refining it, but this works well on ..."
Abstract

Cited by 8 (0 self)
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only if that initial choice is close to a good solution. This thesis describes a method called “affinity propagation ” that simultaneously considers all data points as potential exemplars, exchanging realvalued messages between data points until a highquality set of exemplars and corresponding
REPORTS Clustering by Passing Messages Between Data Points
"... Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars ” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initi ..."
Abstract
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if that initial choice is close to a good solution. We devised a method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Realvalued messages are exchanged between data points until a highquality set of exemplars and corresponding clusters gradually emerges
Decentralized estimation in an inhomogeneous sensing environment
 IEEE TRANS. INF. THEORY
, 2005
"... We consider decentralized estimation of a noisecorrupted deterministic parameter by a bandwidthconstrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varyi ..."
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Cited by 45 (9 self)
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network due to its requirement to transmit realvalued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signaltonoise ratio (SNR). The resulting
and
, 1999
"... We consider the problem of approximating the maximum of the sum of m Lipschitz continuous functions. The values of each function are assumed to reside at a different memory element. A single processing element is designated to approximate the value of the maximum of the sum of these functions by ado ..."
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by adopting a certain protocol. Under certain assumptions on the class of permissible protocols, we obtain the minimum number of realvalued messages that has to be transferred between the processing element and the memory elements in order to find the desired approximation of this maximum. In particular, we
On the Communication Complexity of Solving a Polynomial Equation
, 1991
"... This paper considers the problem of evaluating a function f(x, y) (x E ', y E ff) using two processors P, and P., assuming that processor P, (respectively, P 2) has access to input x (respectively, y) and the functional form of f A new general lower bound is established on the communication co ..."
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Cited by 5 (3 self)
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complexity (i.e., the minimum number of realvalued messages that have to be exchanged). The result is then applied to the case where f(x, y) is defined as a root z of a polynomial equation x, + v,)z'=0 and a lower bound of n is obtained. This is in contrast to the f7(1) lower bound obtained by applying
IMA Seminar Series 2008/2009 Contact:
, 2009
"... The Affinity Propagation algorithm is a novel clustering method proposed by Frey and Dueck in 2007 [1], which combines advantages of both affinitybased clustering (like Hierarchical algorithms) and modelbased clustering (like ExpectationMaximisation). The method takes as input measures of similar ..."
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of similarity between pairs of data points and realvalued messages are exchanged between data points until a set of centres (called exemplars) and corresponding clusters gradually emerge. The Affinity Propagation algorithm also provides a procedure to determine the number of clusters to be considered
Results 1  10
of
524