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Nonparametric decentralized detection using kernel methods
2005
IEEE Transactions on Signal Processing
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We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. ...
We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels, and analyze its computational and statistical properties both theoretically and empirically. ...
In the decentralized setting, however, it is only relatively recently that nonparametric methods for detection have been explored. ...
doi:10.1109/tsp.2005.857020
fatcat:jcporaoy5bbltkxj7lgml5lu7q
Distributed learning in wireless sensor networks
2006
IEEE Signal Processing Magazine
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Applications such as these pave the way for a nonparametric study of distributed detection and estimation. ...
The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions ...
The fusion center uses reproducing kernel methods for learning, with a kernel designed using signal-strength measurements. ...
doi:10.1109/msp.2006.1657817
fatcat:xdabigmwbbgidjpte427qs6ptm
Page 569 of Mathematical Reviews Vol. , Issue 87a
[page]
1987
Mathematical Reviews
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With a slight correction of usual white noise concepts the paper presents a model for the identifi- cation of Raibman kernels using the correlational and dispersional methods.” R. S. ...
569 93E Stochastic systems and control
This paper presents a new two-stage method for decentralized es- timation of large implicit systems. ...
Towards Information Privacy for the Internet of Things
[article]
2017
arXiv
pre-print
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To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. ...
However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. ...
A nonparametric decentralized detection method was introduced by [33] , which proposed the use of a kernel-based method to learn the optimal sensor decision rules from a given set of labeled training ...
arXiv:1611.04254v2
fatcat:toqsmchm4fafbmgloejyxfdxse
Decentralized detection and classification using kernel methods
2004
Twenty-first international conference on Machine learning - ICML '04
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We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. ...
We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels, and analyze its computational and statistical properties both theoretically and empirically. ...
In the decentralized setting, however, it is only relatively recently that nonparametric methods for detection have been explored. ...
doi:10.1145/1015330.1015438
dblp:conf/icml/NguyenWJ04
fatcat:my4iafddwves7azebbmpxj5eue
Nonparametric Probability Density Estimation for Sensor Networks Using Quantized Measurements
2007
2007 41st Annual Conference on Information Sciences and Systems
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We develop a nonparametric method for estimating the probability distribution function (pdf) describing the physical phenomenon measured by a sensor network. ...
Numerical simulations demonstrate the performance of the proposed method. ...
Distributed kernel-based learning and decentralized detection are studied in [1] and [8] - [10] . ...
doi:10.1109/ciss.2007.4298410
dblp:conf/ciss/DogandzicZ07
fatcat:dunppkyqevd3ldeolxgq3qje2u
Decentralized Nonparametric Multiple Testing
[article]
2018
arXiv
pre-print
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design of the algorithm which might otherwise seem daunting using classical approach and notations. ...
Based on the nonparametric functional statistical viewpoint of large-scale inference, started in Mukhopadhyay (2016), this paper furnishes a new computing model that brings unexpected simplicity to the ...
Algorithm: Decentralized Nonparametric Multiple Testing Engine Step 1. ...
arXiv:1805.02075v1
fatcat:et6vzxjvnvcuviytuemzglriam
Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks
2010
IEEE Signal Processing Letters
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Index Terms-Consensus, distributed learning, kernel methods, sensor networks. ...
Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. ...
In this paper, we focus on nonparametric estimation, particularly on kernel methods for regression [8] . Kernel methods are universal classification and estimation algorithms. ...
doi:10.1109/lsp.2010.2040926
fatcat:itvfsd4uyrfvpod57yeaoezbey
SGR: A New Efficient Kernel for Outlier Detection in Sensor Data Minimizing Mise
2015
Sensors & Transducers
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In this paper, we propose a novel unsupervised algorithm for detecting outliers based on density by coupling two principles: first, kernel density estimation and second assigning an outlier score to each ...
Outliers, if neglected as erroneous data, may result in failure to detect important phenomenon. ...
An outlier detection method is also proposed. The proposed algorithm uses our proposed kernel function to construct density estimates. The detection method has two completely decoupled steps. ...
doaj:12754ed06a46488ba8446f0b1f0cd3b1
fatcat:tb3wrzolkvhcthbczdjqwb24l4
Nonparametric distributed sequential detection via universal source coding
2013
2013 Information Theory and Applications Workshop (ITA)
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These algorithms are also compared with the tests using various other nonparametric estimators. Finally a decentralized version utilizing spatial diversity is also proposed and analysed. ...
These algorithms are primarily motivated from spectrum sensing in Cognitive Radios and intruder detection in wireless sensor networks. ...
For nonparametric sequential methods, [14] provides separate algorithms for different setups like changes in mean, changes in variance etc. ...
doi:10.1109/ita.2013.6502977
dblp:conf/ita/SreedharanS13
fatcat:6lg5rxwydredvfmi2uugh2i2tq
Diffusion adaptation over networks with kernel least-mean-square
2015
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
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Most of works focus on distributed estimation methods of linear regression models. ...
In this paper, we derive functional diffusion strategies in reproducing kernel Hilbert spaces. ...
To address such situations, nonparametric methods based on kernel functions were primarily considered for decentralized detection and estimation over networks [1, 5] . ...
doi:10.1109/camsap.2015.7383775
dblp:conf/camsap/GaoCRH15
fatcat:n7umvtr7wbbynnvkcs4jhk5vxi
Nonparametric time series forecasting with dynamic updating
2011
Mathematics and Computers in Simulation
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We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. ...
The methods are demonstrated using monthly sea surface temperatures from 1950 to 2008. ...
Density forecasts As a by-product of the nonparametric bootstrap method, we can produce kernel density plots for visualizing density forecasts using the bootstrapped forecast variants. ...
doi:10.1016/j.matcom.2010.04.027
fatcat:ly2ss5q66nevxfwum7x3vgqzaq
On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
[article]
2019
arXiv
pre-print
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For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. ...
In a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. ...
We first study decentralized detection that preserves only data privacy using the local differential privacy metric. ...
arXiv:1811.10322v3
fatcat:nqjqyqp35rfypfwcge3lwi3mei
Stein Variational Belief Propagation for Multi-Robot Coordination
[article]
2024
arXiv
pre-print
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Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. ...
In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. ...
In contrast, our approach can be used with any differentiable factor and uses a more flexible nonparametric distribution.
III. BACKGROUND A. ...
arXiv:2311.16916v2
fatcat:glveizvwfve6vmtil73jn5umpy
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing
[article]
2022
arXiv
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K-Boot models reward with a kernel smoother on similar past samples selected by k-NN, and Bootstrap Thompson Sampling for exploration. ...
To optimally recommend SMEs while simultaneously learning the true eligibility status, we propose to formulate the routing problem with a nonparametric contextual bandit algorithm (K-Boot) plus an eligibility ...
Kernel-based Bandit uses a nonparametric reward estimator via kernel functions, combined with an exploration policy like TS or UCB. Gaussian Process (GP) has wide applications in bandit domain, e.g. ...
arXiv:2209.05278v1
fatcat:ws3orrpm4vb55m3jj3ytbnthjq
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