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On the Lower Bound of Local Optimum in k-means Algorithm

by Zhenjie Zhang, Bing Tian, Dai Anthony, K. H. Tung , 2006
"... The k-means algorithm is a popular clustering method used in many different fields of computer science, such as data mining, machine learning and information retrieval. However, the k-means algorithm is very likely to converge to some local optimum which is much worse than the desired global optimal ..."
Abstract - Cited by 6 (3 self) - Add to MetaCart
The k-means algorithm is a popular clustering method used in many different fields of computer science, such as data mining, machine learning and information retrieval. However, the k-means algorithm is very likely to converge to some local optimum which is much worse than the desired global

Robust and Locally-Optimum Decentralized Detection with Censoring Sensors

by Swaroop Appadwedula, Venugopal V. Veeravalli, Douglas L. Jones - in 5 th Int. Conf. on Information Fusion , 2002
"... In this paper, we examine decentralized detection problems in which a send/no-send rate (censoring rate) on the sensor decisions replaces the communication constraint of D-level sensor decisions in canonical parallel decentralized detection problems. Rago et. al. introduced the censoring problem to ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
scenarios. Both a robust formulation of the censoring problem, and a locally-optimum formulation of the censoring problem are considered. We nd conditions under which the robust censoring problem can be solved by designing for the least-favorable distributions from the uncertainty classes. For the locally-optimum

LOCALLY OPTIMUM ESTIMATION IN WIRELESS SENSOR NETWORKS

by unknown authors
"... A locally optimum approach for estimating a nonrandom pa-rameter lying in some small neighborhood of a known nom-inal value is considered. Reference is made to a decentral-ized estimation problem in the context of wireless sensor net-works, and particular attention is paid to the design of the quant ..."
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A locally optimum approach for estimating a nonrandom pa-rameter lying in some small neighborhood of a known nom-inal value is considered. Reference is made to a decentral-ized estimation problem in the context of wireless sensor net-works, and particular attention is paid to the design

Local Optimum Based Power Allocation Approach for Spectrum Sharing in Unlicensed Bands

by Furqan Ahmed , Olav Tirkkonen
"... Abstract. We present a novel local optimum based power allocation approach for spectrum sharing in unlicensed frequency bands. The proposed technique is based on the idea of dividing the network in a number of smaller sub-networks or clusters. Sum capacity of each cluster is maximized subject to co ..."
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Abstract. We present a novel local optimum based power allocation approach for spectrum sharing in unlicensed frequency bands. The proposed technique is based on the idea of dividing the network in a number of smaller sub-networks or clusters. Sum capacity of each cluster is maximized subject

Locally Optimum Adaptive Signal Processing Algorithms

by George V. Moustakides
"... Abstract—We propose a new analytic method for comparing constant gain adaptive signal processing algorithms. Specifically, estimates of the convergence speed of the algorithms allow for the definition of a local measure of performance, called the efficacy, that can be theoretically evaluated. By def ..."
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Abstract—We propose a new analytic method for comparing constant gain adaptive signal processing algorithms. Specifically, estimates of the convergence speed of the algorithms allow for the definition of a local measure of performance, called the efficacy, that can be theoretically evaluated

Sequential Detection of Signals with Locally Optimum Test Statistic

by Sang Won Choi, Iickho Song, Jinsoo Bae
"... Based on local optimality, a novel sequential detection scheme is proposed in this paper. The performance of the proposed scheme is compared with that of the sequential probability ra-tio test (SPRT) and truncated SPRT (TSPRT). The proposed scheme is shown to have higher efficiency, lower complexity ..."
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Based on local optimality, a novel sequential detection scheme is proposed in this paper. The performance of the proposed scheme is compared with that of the sequential probability ra-tio test (SPRT) and truncated SPRT (TSPRT). The proposed scheme is shown to have higher efficiency, lower

Global Data Assimilation by Local Optimum Interpolation

by National Weather Service, R. D. Mcpherson, K. H. Bergman, R. E. Kistler, G. E. Rasch, D. S. Gordon , 1977
"... This is an informal unreviewed manuscript primarily intended for the exchange of information among NMC staff members. ..."
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This is an informal unreviewed manuscript primarily intended for the exchange of information among NMC staff members.

Estimating Local Optimums in EM Algorithm over Gaussian Mixture Model

by Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung
"... EM algorithm is a very popular iterationbased method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
EM algorithm is a very popular iterationbased method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - 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 error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
tiply connected networks: When loops are present, the network is no longer singly connected and local propaga tion schemes will invariably run into trouble . We believe there are general undiscovered theorems about the performance of belief propagation on loopy DAGs. These theo rems, which may have

Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study

by Luis Paquete, Marco Chiarandini, Thomas Stützle - METAHEURISTICS FOR MULTIOBJECTIVE OPTIMIZATION, LECTURE , 2004
"... In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward extensions of local search algorithms for the single objective case. The performance of the local search algorithms is illustrated by experimental results obtained for well k ..."
Abstract - Cited by 22 (5 self) - Add to MetaCart
In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward extensions of local search algorithms for the single objective case. The performance of the local search algorithms is illustrated by experimental results obtained for well
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