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distributed Inference in Tree Networks Using Coding Theory

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第14期63卷 3715-3726页

作者： Kailkhura, Bhavya Vempaty, Aditya Varshney, Pramod K. Syracuse Univ Dept Elect & Comp Engn Syracuse NY 13244 USA

In this paper, we consider the problem of distributed inference in tree based networks. In the framework considered in this paper, distributed nodes make a 1-bit local decision regarding a phenomenon before sending it to the fusion center (FC) via intermediate nodes. We propose the use of coding theory based techniques to solve this distributed inference problem in such structures. Data is progressively compressed as it moves towards the FC. The FC makes the global inference after receiving data from intermediate nodes. Data fusion at nodes as well as at the FC is implemented via error correcting codes. In this context, we analyze the performance for a given code matrix and also design the optimal code matrices at every level of the tree. We address the problems of distributed classification and distributed estimation separately and develop schemes to perform these tasks in tree networks. The proposed schemes are of practical significance due to their simple structure. We study the asymptotic inference performance of our schemes for two different classes of tree networks: fixed height tree networks, and fixed degree tree networks. We derive the sufficient conditions under which the proposed schemes are asymptotically optimal.

关键词： distributed classification distributed estimation error correcting codes information fusion tree networks wireless sensor networks

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RD-SVM: A RESILIENT distributed SUPPORT VECTOR MACHINE

RD-SVM: A RESILIENT DISTRIBUTED SUPPORT VECTOR MACHINE

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhixiong Yang Waheed U. Bajwa Dept. of Electrical and Computer Engineering Rutgers The State University of New Jersey USA

ISBN: (纸本)9781479999897

Support vector machines (SVMs) are one of the most widely used supervised learning algorithms for classification problems. Recent years have witnessed an increasing interest in distributed variants of SVMs, in which the (labeled) training data is distributed across different nodes. While a number of algorithms have been developed in this regard, they all make the simplified assumption that every node in the network operates as intended. In many applications, however, it is common for some of the nodes to undergo failures due to faulty equipment, cyber attacks, etc., and inject faulty data into the network. This kind of failure, termed Byzantine failure, is impossible to protect against using existing distributed SVM algorithms. This paper revisits the problem of distributed SVM under the possibility of Byzantine failures in the network. In this regard, it proposes a novel algorithm for distributed SVM that remains resilient to Byzantine failures as long as the number of faulty nodes in the network is not too large. Numerical results on real-world data confirm the superiority of the proposed algorithm over existing approaches.

关键词： Byzantine fault tolerance distributed classification distributed consensus support vector machine Support Vector Network Resilience Fault tolerance Distribute Ganglia machine failures algorithms

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distributed Pattern Recognition within DHT-Based Networks for Imbalanced Datasets

Distributed Pattern Recognition within DHT-Based Networks fo...

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2016 First IEEE International Conference on Computer Communication and the Internet

作者： Amiza Amir Bala Srinivasan Asad I.Khan School of Computer and Communication Engineering Universiti Malaysia Perlis Faculty of Information Technology Monash University

ISBN: (纸本)9781467385169

This paper studies the accuracy of a fullydistributed pattern recognition algorithm,namely the P2P-GN,with imbalanced datasets problem which is often neglected by most of the available distributed algorithms.A major distinction of the P2P-GN compared to the other approaches is that it forms a single global classifier,instead of building many local classifiers(one at every site).Fine-granularity components of the classifier are distributed across the network by using distributed Hash Table(DHT)—which provides efficient linking to these components and ensures the system remains *** experimental results also show that the P2P-GN can produce highly accurate results despite imbalanced data distribution compared to other distributed algorithms(Ivote-DPV,ensemble ID3,ensemble k-NN,and ensemble na?ve Bayes).

关键词： P2P-based classification distributed machine learning efficient distributed algorithm distributed classification

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POWER SCHEDULING FOR distributed MULTIPLE-HYPOTHESIS DETECTION BY TASK-SPECIFIC INFORMATION 5

POWER SCHEDULING FOR DISTRIBUTED MULTIPLE-HYPOTHESIS DETECTI...

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IEEE International Conference on Signal Processing, Communications and Computing ICSPCC 2015

作者： Kim, Hyoung-soo Yang, Sung-Il Hanyang Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781479989201

We introduce a new information theoretic power allocation scheme applicable to distributed multiple-hypothesis detection systems communicating over slow fading channels. In earlier work, it was demonstrated that performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system and the J-divergence method is extended for a distributed multiple hypothesis detection system by defining pairwise sums of the J-divergences. However, the pairwise sum measure does not provide a tight bound. Basically, the more hypotheses we adopt, the less efficient the optimization is. Thus, we derive a more efficient classification-oriented information measure for power optimization of distributed multiple-hypothesis system by introducing a virtual decider variable. The virtual decider variable is directly related with classification task. Various numerical results are also shown to compare the performances.

关键词： optimal power allocation distributed detection multiple hypotheses task-specific information classification distributed classification mutual information slow fading Rayleigh fading channel

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distributed Data classification in Sensor Networks

Distributed Data Classification in Sensor Networks

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29th ACM SIGACT-SIGOPS Symposium on Principles of distributed Computing

作者： Eyal, Ittay Keidar, Idit Rom, Raphael Technion Israel Inst Technol IL-32000 Haifa Israel

ISBN: (纸本)9781605588889

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we define the distributed classification problem, in which numerous interconnected nodes compute a classification of their data, i.e., partition these values into multiple collections, and describe each collection concisely. We present a generic algorithm that solves the distributed classification problem and may be implemented in various topologies, using different classification types. For example, the generic algorithm can be instantiated to classify values according to distance, like the famous k-means classification algorithm. However, the distance criterion is often not sufficient to provide good classification results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for classification decisions. Simulations show the robustness and speed of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, classification criterion and collection representation, in fully asynchronous settings.

关键词： distributed classification Gossip

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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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Power-Aware Activity Monitoring Using distributed Wearable Sensors

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2014年第4期44卷 537-544页

作者： Ghasemzadeh, Hassan Panuccio, Pasquale Trovato, Simone Fortino, Giancarlo Jafari, Roozbeh Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Univ Calabria Dipartimento Informat Elettron & Sistemist I-87036 Arcavacata Di Rende Italy Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

Monitoring human movements using wireless wearable sensors finds applications in a variety of domains including healthcare and wellness. In these systems, sensory devices are tightly integrated with the human body and infer status of the user through signal and information processing. Typically, highly accurate observations can be made at the cost of deploying a sufficiently large number of sensors, which in turn results in increased energy consumption of the system and reduced adherence to using the system. Therefore, optimizing power consumption of the system while maintaining acceptable accuracy plays a crucial role in realizing these stringent resource constraint systems. In this paper, we present an activity monitoring approach that minimizes power consumption of the system subject to a lower bound on the classification accuracy. The system utilizes computationally simple template-matching blocks that perform classifications on individual sensor nodes. The system further employs a boosting approach to enhance accuracy of the distributed classifier by selecting a subset of sensors optimized in terms of power consumption and capable of achieving a given lower bound accuracy criterion. A proof-of-concept evaluation with three participants performing 14 transitional actions was conducted, where collected signals were segmented and labeled manually for each action. The results indicated that the proposed approach provides more than a 65% reduction in the power consumption of the signal processing, while maintaining 80% sensitivity in classifying human movements.

关键词： Action recognition AdaBoost distributed classification low-power design real-time embedded systems signal processing wearable computing

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A Computational model based on Cooperative Mobile Agents for Big Data image Segmentation 2

A Computational model based on Cooperative Mobile Agents for...

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2014 Second World Conference on Complex Systems (WCCS)

作者： Benchara, Fatema Zahra Youssfi, Mohamed Bouattane, Omar Ouajji, Hassan Bensalah, Mohammed Ouadi Univ Hassan II Casablanca ENSET Mohammedia Lab SSDIA Casablanca Morocco Univ Mohammed V AGDAL FSR Rabat Morocco

ISBN: (纸本)9781479946471

The aim of this paper is to present interesting results obtained by implementing big data classification algorithm using a cooperative mobile agents model. In this work we focused on the application of this classification for Magnetic Resonance Images (MRI) segmentation which is presented based on parallel fine grained c-means algorithm. This algorithm is converged to distributed classification c-means algorithm in order to be implemented on a parallel and distributed virtual machine based on mobile agents. The big data MRI image to be processed is split into small elementary images by the host agent. The obtained list of item images is distributed to mobile agents deployed on each node of the distributed system. Each agent is asked to perform the execution of the c-means algorithm using its assigned image item and return its elementary results to the host agent which computes the current global class centers and newly distributes them. This process is repeated until the convergence of the distributed algorithm. The experimental results show that the distributed classification method can improve the big data segmentation.

关键词： Image Segmentation c-means algorithm mobile agent distributed classification multi agent system Cardiac MRI image

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Maximum Entropy Modeling for distributed classification, Regression, and Interaction Discovery

Maximum Entropy Modeling for Distributed Classification, Reg...

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作者： Zhang, Yanxin PennState University Libraries

学位级别：Doctor of Philosophy

The maximum entropy (ME) principle has been widely applied to specialized applications in statistical learning and pattern recognition. The concept of ME method is to find a probability distribution that satisfies whatever information is available from known data in the form of constraints. The ME solution is the unique Gibbs distribution that maximizes the likelihood of the training data. In this dissertation, we develop ME methods with applications to three important tasks, i.e., distributed classification, regression, and identification of feature interactions. In the distributed classification paradigms, where common labeled data may be not available for designing classifier ensemble, traditional fixed decision aggregation such as voting, averaging, or naive Bayes rules could not account for class prior mismatch or classifier dependencies. Previous transductive learning strategies have several drawbacks, e.g., feasibility of the constraints was not guaranteed and heuristic learning was applied. We overcome these problems by proposing a transductive maximum entropy (TME) model for designing aggregation to satisfy the constraints in local classifiers. We augment the test set support to ensure the feasibility of the constraints and develop transductive iterative scaling (TIS) algorithm for optimal solution. This method is shown to achieve improved decision accuracy over the earlier transductive approaches and fixed rules on a number of UC Irvine data ***, ME models have been developed for classification on discrete feature spaces, i.e., both the output variable and input features are categorical or ordinal. We extend ME model for the regression problem, where the output variable and input features are mixed continuous-discrete valued. We propose a hierarchical maximum entropy (HME) model for regression in building a posterior model for the output variable, which encodes constraints involving hierarchical derived features that are obtained by agglomerativ

关键词： interaction discovery maximum entropy distributed classification 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

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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