An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series

Guo, Yan; Song, Xiaoxiang; Li, Ning; Fang, Da‐Gang

doi:10.1109/access.2018.2873414

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…At the same time, it use the second-order difference matrix as the sparse transformation matrix. The sparsity of the data transformed by the second-order difference matrix can reach 5% for perception data [25]. A principal component analysis algorithm based on singular value decomposition is presented in [25].…”

Section: A Task Allocation Based On Compressive Sensingmentioning

confidence: 99%

“…The sparsity of the data transformed by the second-order difference matrix can reach 5% for perception data [25]. A principal component analysis algorithm based on singular value decomposition is presented in [25]. We used this method to decompose different types of historical data to get a sparse transformation matrix.…”

Section: A Task Allocation Based On Compressive Sensingmentioning

confidence: 99%

“…the original data can be recovered without error [25]. For the number of participants needed in DS-UPP, we have the following theorem, which gives its lower bound and upper bound.…”

Section: Theoretical Analysismentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

Song

Chen

2020

IEEE Access

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To minimize the sensing cost in MCS while preserving the participants' privacy, in this paper we propose a Data Sensing mechanism with User Privacy Preserved (DS-UPP). We introduce edge computing into MCS to support task allocation and user privacy protection. In DS-UPP, based on compressive sensing theory we minimize the amount of data needed to be submitted. We also design an algorithm based on local differential privacy theory. Selected participants only need to submit their real data along with the reconstructed data generated by the algorithm. It is proved that DS-UPP satisfies εdifferential privacy. We give the mathematical lower bound and upper bound of the number of participants needed for task accomplishment with the constraints that privacy budget is ε and recovery error of task data is 0, as well as the average amount of data that should be submitted by a participant. We also evaluate the performance of DS-UPP through simulations. Compared with the existing method PrivKV, DS-UPP can reduce the needed data amount by about 90% on the average while guarantee users' privacy preserved.

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Section: A Task Allocation Based On Compressive Sensingmentioning

confidence: 99%

Section: A Task Allocation Based On Compressive Sensingmentioning

confidence: 99%

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Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

Song

Chen

2020

IEEE Access

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“…when 4 − 3 > 0. The notationX represents the final reconstruction, 3 , 4 , N , and are given in (33), (47), and (31), respectively, and…”

Section: Theorem 7: When the Termination Conditionmentioning

confidence: 99%

“…This process introduces a high physical complexity to the sampling hardware and is difficult to implement [27]- [29]. To relieve complexity of the sampling implementation, researchers have employed the Kronecker structure for the sensing matrix and sparsifying base to replace the distributed scheme for global operation [30]- [33]. Under this framework, conventional greedy algorithms are hindered by the considerable computational complexity of the data reconstruction [23], [30], [34].…”

Section: Introductionmentioning

confidence: 99%

Atom-Refined Multiway Greedy Algorithm for Tensor-Based Compressive Sensing

Zhao

Liu

et al. 2019

IEEE Access

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In this paper, we develop a novel multiway greedy algorithm, named atom-refined multiway orthogonal matching pursuit, for tensor-based compressive sensing (TCS) reconstruction. The alternative supports of each dimension are selected using the respective inner product tensors and refined via a global least square coefficients tensor. For each inner product tensor, the Frobenius-norm (F-norm) of the tensor bands, instead of the largest magnitude entry, is employed to measure the correlation between the atoms and the residual. Theoretical analysis shows that the proposed algorithm could guarantee to exactly reconstruct an arbitrary multi-dimensional block-sparse signal in the absence of noise, provided that the sensing matrices for each dimension satisfy restricted isometry properties with constant parameters. The maximum required number of iterations for exact reconstruction shows an approximate logarithmic growth as the signal size increases. Furthermore, under the noise condition, it is presented that the F-norm of the reconstruction error can be upper-bounded by using the F-norm of noise and the restricted isometry constants of sensing matrices for each dimension. The simulation results demonstrate that the proposed algorithm exhibits obvious advantages as regards both reconstruction accuracy and speed compared with the existing multiway greedy algorithms. Besides TCS, the proposed algorithm also has the potential to be applied in diverse fields, such as hyperspectral image processing and tensor-based dictionary learning.

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Imputation of missing data in time series by different computation methods in various data set applications

Magare¹,

Labde²,

Gofane³

et al. 2020

ITM Web Conf.

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In a modern technology generation, big volumes of data are evolved under numerous operations compared to an earlier era. However, collection of data without missing single value, is a great challenge ahead. In practice, there are many solutions suggested to avoid the missing values in time series applications. The existing methods used in imputation and their prediction with time series, varies with applications. The existing methods mostly available for imputation are least squares support vector machine (LSSVM), autoregressive integrated moving average models (ARIMA), Artificial Neural Network (ANN), Artificial Intelligence (AI) techniques, state space models, Kalman filtering and fuzzy model. The extensive experimental application data is used to analyze these methods. In addition, a synthetic set of data can also be used to forecast missing value, which improves performance of imputation methods in time series. In this paper, predominantly used imputation methods have been listed with their fundamental computational information along with their verification on set of data mentioned.

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An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series

Cited by 5 publications

References 30 publications

Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

Atom-Refined Multiway Greedy Algorithm for Tensor-Based Compressive Sensing

Imputation of missing data in time series by different computation methods in various data set applications

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