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In-cluster vector evaluated particle swarm optimization for distributed regression in WSNs

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2014年第Jun.期42卷 80-91页

作者： Shakibian, Hadi Charkari, Nasrollah Moghadam Tarbiat Modares Univ Fac Elect & Comp Engn Parallel Proc Lab 6 51 Tehran Iran

Conventional methods address data modeling in WSNs by converting a learning problem into an optimization task. These methods are mainly based on Gradient Descent and Nelder-Mead Simplex optimization techniques. They are energy efficient, but with drawbacks: the accuracy of the induced models is usually lower than that of the centralized technique;and the latency is proportional to the network size. In this paper, we propose a novel distributed method for parametric regression in a clustered WSN using particle swarm optimization. Two main contributions of this paper are as follows: First, we re-formulate the distributed regression in WSNs as a multiobjective optimization (MO) problem in which an objective is dedicated to each cluster. Second, we propose a distributed algorithm based on the Vector Evaluated Particle Swarm Optimization method to address the MO problem in two phases. The proposed algorithm obtains a set of candidate network regressors and computes the final model using a weighted averaging rule. We compare the prediction accuracy, latency, and energy consumption of our algorithm against its popular distributed counterparts and the centralized technique using a real-world and an artificial data set. The experimental results show that our algorithm outperforms the existing approaches in prediction accuracy while its energy consumption and latency are acceptable. We also evaluate the effect of the number of particles, swarm topologies, and clustering granularity on the performance of the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： distributed regression Particle swarm optimization Wireless sensor networks Multiobjective optimization distributed optimization

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Combining distributed regression and propensity scores: a doubly privacy-protecting analytic method for multicenter research

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CLINICAL EPIDEMIOLOGY 2018年 10卷 1773-1786页

作者： Toh, Sengwee Wellman, Robert Coley, R. Yates Horgan, Casie Sturtevant, Jessica Moyneur, Erick Janning, Cheri Pardee, Roy Coleman, Karen J. Arterburn, David McTigue, Kathleen Anau, Jane Cook, Andrea J. Harvard Med Sch Dept Populat Med 401 Pk DrSuite 401 East Boston MA 02215 USA Harvard Pilgrim Hlth Care Inst 401 Pk DrSuite 401 East Boston MA 02215 USA Kaiser Permanente Washington Hlth Res Inst Seattle WA USA StatLog Econometr Inc Montreal PQ Canada Duke Clin & Translat Sci Inst Durham NC USA Kaiser Permanente Southern Calif Pasadena CA USA Univ Pittsburgh Dept Med Pittsburgh PA USA

Purpose: Sharing of detailed individual-level data continues to pose challenges in multicenter studies. This issue can be addressed in part by using analytic methods that require only summary-level information to perform the desired multivariable-adjusted analysis. We examined the feasibility and empirical validity of 1) conducting multivariable-adjusted distributed linear regression and 2) combining distributed linear regression with propensity scores, in a large distributed data network. Patients and methods: We compared percent total weight loss 1-year postsurgery between Roux-en-Y gastric bypass and sleeve gastrectomy procedure among 43,110 patients from 36 health systems in the National Patient-Centered Clinical Research Network. We adjusted for baseline demographic and clinical variables as individual covariates, deciles of propensity scores, or both, in three separate outcome regression models. We used distributed linear regression, a method that requires only summary-level information (specifically, sums of squares and cross products matrix) from sites, to fit the three ordinary least squares linear regression models. A comparison set of analyses that used pooled deidentified individual-level data from sites served as the reference. Results: distributed linear regression produced results identical to those from the corresponding pooled individual-level data analysis for all variables in all three models. The maximum numerical difference in the parameter estimate or standard error for all the variables was 3x10(-11) across three models. Conclusion: distributed linear regression analysis is a feasible and valid analytic method in multicenter studies for one-time continuous outcomes. Combining distributed regression with propensity scores via modeling offers more privacy protection and analytic flexibility.

关键词： distributed regression propensity score distributed data networks privacy-protecting methods

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Steepest Descent based Optimization for distributed regression in Wireless Sensor Networks

Steepest Descent based Optimization for Distributed Regressi...

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Hou, Chaojun Guo, Xuemei Wang, Guoli Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781424421077

This paper presents a steepest descent based algorithm for the distributed optimization towards to data regression modeling in wireless sensor networks(WSNs). In doing this, the junction tree based routing structure is employed to organize the nodes in coordination to accomplish the in-network implementation of the distributed iterative scheme. Experimental results are reported to demonstrate the effectiveness of the proposed scheme.

关键词： wireless sensor networks data collection distributed regression steepest descent

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Steepest Descent based Optimization for distributed regression in Wireless Sensor Networks

Steepest Descent based Optimization for Distributed Regressi...

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The 4th International Conference on Wireless Communications, Networking and Mobile Computing(第四届IEEE无线通信、网络技术及移动计算国际会议)

作者： Chaojun Hou Xuemei Guo Guoli Wang School of Information Science & Technology Sun Yat-Sen University Guangzhou 510275 China PR

关键词： wireless sensor networks data collection distributed regression steepest descent

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Auto-tuning procedures for distributed nonparametric regression algorithms

Auto-tuning procedures for distributed nonparametric regress...

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European Control Conference (ECC)

作者： Varagnolo, Damiano Pillonetto, Gianluigi Schenato, Luca Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden Univ Padua Dept Informat Engn I-35100 Padua Italy

ISBN: (纸本)9783952426937

We propose a distributed regression algorithm with the capability of automatically calibrating its parameters during its on-line functioning. The estimation procedure corresponds to a Regularization Network, i.e., the structural form of the estimator is a linear combination of basis functions which coefficients are computed by solving a linear system. The automatic tuning strategy instead constructs and then exploits opportune bounds on the distance between the distributed estimation results and the unknown centralized optimal estimate that would be computed processing the whole dataset at once. By numerical simulations we show how the proposed procedure allows the sensor networks to effectively self-tune the parameters of the distributed regression scheme by simple consensus strategies.

关键词： distributed regression distributed calibration self-organizing sensor networks regularization networks non-parametric estimation

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Data-driven confidence bands for distributed nonparametric regression 33

Data-driven confidence bands for distributed nonparametric r...

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33rd Conference on Learning Theory (COLT)

作者： Avanesov, Valeriy WIAS Berlin Berlin Germany

Gaussian Process regression and Kernel Ridge regression are popular nonparametric regression approaches. Unfortunately, they suffer from high computational complexity rendering them inapplicable to the modern massive datasets. To that end a number of approximations have been suggested, some of them allowing for a distributed implementation. One of them is the divide and conquer approach, splitting the data into a number of partitions, obtaining the local estimates and finally averaging them. In this paper we suggest a novel computationally efficient fully data-driven algorithm, quantifying uncertainty of this method, yielding frequentist L-2-confidence bands. We rigorously demonstrate validity of the algorithm. Another contribution of the paper is a minimax-optimal high-probability bound for the averaged estimator, complementing the known risk bounds.

关键词： Gaussian process regression kernel ridge regression nonparametric regression distributed regression confidence bands bootstrap

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distributed Nonlinear regression Using In-Network Processing With Multiple Gaussian Kernels 18

Distributed Nonlinear Regression Using In-Network Processing...

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18th IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC)

作者： Shin, Ban-Sok Paul, Henning Yukawa, Masahiro Dekorsy, Armin Univ Bremen Dept Commun Engn Bremen Germany Keio Univ Dept Elect & Elect Engn Tokyo Japan

ISBN: (纸本)9781509030095

In this paper, we propose the use of multiple Gaussian kernels for distributed nonlinear regression or system identification tasks by a network of nodes. By employing multiple kernels in the estimation process we increase the degree of freedom and thus, the ability to reconstruct nonlinear functions. For this, we extend the so-called KDiCE algorithm, which allows a distributed regression of nonlinear functions but uses a single kernel only, to multiple kernels. We corroborate our proposed scheme by numerical evaluations for the reconstruction of nonlinear functions both static and time-varying. We achieve performance gains for both cases, in particular for the tracking of a time-varying nonlinear function.

关键词： In-network processing distributed regression kernel least-squares multiple kernels

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A new class of distributed optimization algorithms: application to regression of distributed data

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OPTIMIZATION METHODS & SOFTWARE 2012年第1期27卷 71-88页

作者： Ram, S. Sundhar Nedic, A. Veeravalli, V. V. Univ Illinois Ind & Enterprise Syst Engn Dept Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

In a distributed optimization problem, the complete problem information is not available at a single location but is rather distributed among different agents in a multi-agent system. In the problems studied in the literature, each agent has an objective function and the network goal is to minimize the sum of the agents' objective functions over a constraint set that is globally known. In this paper, we study a generalization of the above distributed optimization problem. In particular, the network objective is to minimize a function of the sum of the individual objective functions over the constraint set. The 'outer' function and the constraint set are known to all the agents. We discuss an algorithm and prove its convergence, and then discuss extensions to more general and complex distributed optimization problems. We provide a motivation for our algorithms through the example of distributed regression of distributed data.

关键词： distributed optimization convex optimization distributed regression

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distributed INFERENCE OVER regression AND CLASSIFICATION MODELS

DISTRIBUTED INFERENCE OVER REGRESSION AND CLASSIFICATION MOD...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Towfic, Zaid J. Chen, Jianshu Sayed, Ali H. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

ISBN: (纸本)9781479903566

We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the rate 1/Ni on average and with high probability, where N is the number of nodes in the network and i is the number of iterations. We derive asymptotic expressions for the expected regularized KL divergence and show that the diffusion strategy can outperform both non-cooperative and conventional centralized strategies, since diffusion implementations can weigh a node's contribution in proportion to its noise level.

关键词： distributed regression distributed classification diffusion adaptation Kullback-Leibler divergence relative entropy

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distributed INFERENCE OVER regression AND CLASSIFICATION MODELS

DISTRIBUTED INFERENCE OVER REGRESSION AND CLASSIFICATION MOD...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zaid J. Towfic Jianshu Chen Ali H. Sayed Electrical Engineering Department University of California Los Angeles

ISBN: (纸本)9781479903573

关键词： distributed regression distributed classification diffusion adaptation Kullback-Leibler divergence relative entropy relative entropy Likelihood Functions Ganglia Noise levels diffusion iterative methods Divergence divergence Divergence divergence Taxonomy

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