Quantizer design for distributed estimation with communication constraints and unknown observation statistics

Megalooikonomou, Vasileios; Yesha, Yaacov

doi:10.1109/26.823548

Cited by 61 publications

(29 citation statements)

References 14 publications

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“…The optimal number of bits to quantize the observations at node k, that is given by the solution to (21), are characterized in the following lemma. Lemma 7: [9] The optimal number of bits used to quantize the observations at the k-th node found by solving (21) where (x) + equals to zero if x < 0 and equals to x otherwise.…”

Section: ) Quantized Local Processingmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

Wimalajeewa¹,

Jayaweera²,

Mosquera³

2009

Sensor and Data Fusion

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Section: ) Quantized Local Processingmentioning

confidence: 99%

“…is the final estimator mapping. There are several quantization schemes proposed in the literature each having its own advantages and disadvantages [9], [22], [21]. For simplicity, throughout this chapter we concentrate on the universal decentralized quantization scheme given in [9].…”

Section: B Ideal Centralized Data Fusionmentioning

confidence: 99%

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

Wimalajeewa¹,

Jayaweera²,

Mosquera³

2009

Sensor and Data Fusion

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“…It uses an idea of iterative encoder and decoder design in General Source Coding (Lam and Reibman 1993;Megalooikonomou and Yesha 2000). The algorithm starts by randomly selecting K ranks and growing a tree limited to assigning one of the K ranks to the leafs.…”

Section: Ranktree Label Ranking Clustering Algorithmmentioning

confidence: 99%

Supervised clustering of label ranking data using label preference information

et al. 2013

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This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers' feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a Learn (2013) 93:191-225 multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.

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“…Applications of Decentralized Estimation can be found in remotesensing, sonar and seismology systems. It has been a focus of considerable research in the past two decades 3,4,5,6,7,8,9,10 .…”

Section: Introductionmentioning

confidence: 99%

“…Here, we consider a more realistic scenario and address the problem using a set of empirical samples of sensor and target measurements 7,8,6 . In many real-world applications it is reasonable to assume that certain amount of source an target data can be collected for purposes of encoder and decoder design.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Estimation using distortion sensitive learning vector quantization

Grbovic

Vučetić

2013

Pattern Recognition Letters

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Quantizer design for distributed estimation with communication constraints and unknown observation statistics

Cited by 61 publications

References 14 publications

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

Supervised clustering of label ranking data using label preference information

Decentralized Estimation using distortion sensitive learning vector quantization

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