A Fused CP Factorization Method for Incomplete Tensors

Wu, Yao; Tan, Huachun; Li, Yong; Zhang, Jian; Ji, Bo

doi:10.1109/tnnls.2018.2851612

Cited by 51 publications

(32 citation statements)

References 55 publications

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“…They introduced dynamic tensor decomposition into the predicted dynamic tensor decomposition method of traffic flow. Y. Wu et al, Q. Zhao et al also proposed a method for incomplete tensors [25], [26] to reduce distortion images, which was also suitable for traffic data filling.…”

Section: A Related Workmentioning

confidence: 99%

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Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Gong

Zhang

2020

IEEE Access

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Due to various uncontrollable factors (such as random faulty acquisition equipment and data distortion), urban traffic flow data inevitably suffers from some form of data loss. Finding an effective filling method to estimate the missing data is of great help to the study of transportation networks. Traffic flow during a day are likely to have its regular peak period and off-peak period. For most regions of the urban road network, normally there is a certain trend in the traffic flow data. In this paper, we propose a data imputation method that employs a tensor decomposition approach, which fully considers the characteristics of the traffic flow in both time and space. The proposed method is based on high order singular value decomposition with soft thresholding core. In this method, traffic data are divided into its main trend part and the residual part, which is called detrending. And tensor decomposition is performed on these two parts separately. For each part, dynamic rank method is used to adjust the rank of tensor decomposition. With the actual 214 anonymous road segments with 10 minutes interval data in Guangdong, China, the highway data with 15 minutes interval in Madrid, Spain, and the traffic flow data from PeMS with 5 minutes interval in California, USA. The results of the different models are discussed in the case of continuous data missing and random data missing by different time intervals. In addition, by comparing with other data imputation methods, our method can fill the missing data with better performance. INDEX TERMS Detrending, incomplete data filling, singular value decomposition, tensor decomposition. CHUANFEI GONG received the B.S. degree in computer science from Tongji University, Shanghai, China, in 2017, where he is currently pursuing the master' degree with the Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science. His research interests include intelligent transportation systems and data mining.

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Section: A Related Workmentioning

confidence: 99%

“…Three models are selected for the comparative experiments, which are the HALRTC, the FCP [25] and the proposed method. For the former two methods, we split the date dimension of the original tensor into the day dimension and the week dimension so as to get a fourway tensor X ∈ R 9 * 7 * 7 * 288 .…”

Section: F Tests On Traffic Flow Data From Pemsmentioning

confidence: 99%

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Gong

Zhang

2020

IEEE Access

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“…Tensor-based methods have been successfully utilized in various fields, such as computer vision [25], deep neural networks [26], and road traffic [27]. Liu et al [28] first defined the nuclear norm of a tensor and translated tensor completion into a convex optimization problem.…”

Section: B Other Tensor-based Solutionsmentioning

confidence: 99%

Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion

Wang

Chen

Wang

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and LF-norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

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“…CANDECOMP/PARAFAC (CP) decompositoin methods [9]- [11], [25], [26] based on CP rank [27]. For a k-way tensor X ∈ R n1×n2×•••×n k , the CP decomposition is written as follows…”

Section: A Cp Decompositionmentioning

confidence: 99%

“…The initial rank is set to 100 in FBCP and FBCPMP [9]. In [12], the minimum and maximum rank are set to r min = (25,5,5) and r max = (50, 20, 20), respectively. The regularization parameter λ in TRPCA [14] and our proposed RTF is set as λ = 1/ max(n 1 , n 2 )n 3 .…”

Section: B Parameters Settingmentioning

confidence: 99%

Robust Tensor Factorization for Color Image and Grayscale Video Recovery

Shi

et al. 2020

IEEE Access

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Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory. Since rank minimization is an NPhard problem, one strategy is that it is converted into a convex relaxation tensor nuclear norm (TNN) that requires the repeated calculation of time-consuming SVD, and the other is to convert it into some product of two smaller tensors that are easy to fall into the local minimum. In order to overcome the above shortcomings, we propose a robust tensor factorization (RTF) model for solving LRTC. In RTF, the noisy tensor data with missing entries is decomposed into low-rank tensor and noisy tensor, and then the lowrank tensor is equivalently decomposed into t-products (essentially vectors convolution) of two smaller tensors: orthogonal dictionary tensor and low-rank representation tensor. Meanwhile, the TNN of low-rank representation tensor is adopted to characterize the low-rank structure of the tensor data for preserving global information. Then, an effective iterative update algorithm based on the alternating direction method of multipliers (ADMM) is proposed to solve RTF. Finally, numerical experiments on image recovering and video completion tasks show the effectiveness of the proposed RTF model compared with several state-ofthe-art tensor completion models.

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A Fused CP Factorization Method for Incomplete Tensors

Cited by 51 publications

References 55 publications

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion

Robust Tensor Factorization for Color Image and Grayscale Video Recovery

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