Greedy sensor selection: Leveraging submodularity

Shamaiah, Manohar; Banerjee, Siddhartha; Vikalo, Haris

doi:10.1109/cdc.2010.5717225

Cited by 262 publications

(251 citation statements)

References 7 publications

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“…Similar greedy techniques have been applied to radar arrays [17,18] where the change of the MMSE is used for target detection. It should also be noted that the utility proposed in our framework differs from other definitions such as [11,19,20] which rely on the concept of submodularity.…”

Section: Introductionmentioning

confidence: 71%

“…Note thatŴ k −q is not equal toŴ k with M q rows removed but it is equal to the re-optimized MMSE estimator that minimizes J k −q in which the sensors of node q are removed. Using (11), the utility of node q with respect to node k's estimation problem is given by…”

Section: Utility For Node Removalmentioning

confidence: 99%

“…In [10] a distributed strategy has been proposed that is compared to a centralized approach but is cast as a multi-armed bandit problem. Other methods are able to evaluate performance bounds compared to the optimal solution [11]. Thatte et.…”

Section: Introductionmentioning

confidence: 99%

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Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

et al. 2014

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A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive nodespecific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection.

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Section: Introductionmentioning

confidence: 71%

Section: Utility For Node Removalmentioning

confidence: 99%

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Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

et al. 2014

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“…This property is utilised in, for example, Perelman et al (2016) and Shamaiah, Banerjee, and Vikalo (2010), and if satisfied, a heuristic function would also be easy to compute. However, the amount of distinguishability that is increased for each fault pair D S∪{y l } i,j (θ ), when adding a sensor y l , depends on the previous selected set of sensors S. If S 1 , S 2 ⊆ S \ {y l } are two sets of sensors such that S 1 ⊆ S 2 , then…”

Section: Distinguishability Bounds and Submodularitymentioning

confidence: 99%

Sensor selection for fault diagnosis in uncertain systems

Jung

Dong

Frisk

et al. 2018

International Journal of Control

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Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time. ARTICLE HISTORY

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“…We distinguish this problem from the data summarization of large datasets [3], [20], which is concerned with reducing the size of the data irrespective of the target, and from the task of selecting a subset of features, which is encountered in dimensionality reduction problems [30], [6], [27], [25]. In contrast to the existing work, we propose an algorithm that can return any desired size of the subset and that takes into account both the proximity of elements to the boundary (using accelerated methods for nearest neighbor search) as well as providing a diverse subset.…”

Section: Introductionmentioning

confidence: 99%

Data subset selection for efficient SVM training

Mourad

Tewfik

Vikalo

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Training a support vector machine (SVM) on large data sets is a computationally intensive task. In this paper, we study the problem of selecting a subset of data for training the SVM classifier under requirement that the loss of performance due to training data reduction is low. A function quantifying suitability of a selected subset is proposed, and a greedy algorithm for solving the subset selection problem is introduced. The algorithm is evaluated on hand digit recognition and other binary classification tasks, and its performance is compared to stratified sampling methods.

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Greedy sensor selection: Leveraging submodularity

Cited by 262 publications

References 7 publications

Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

Sensor selection for fault diagnosis in uncertain systems

Data subset selection for efficient SVM training

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