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Traffic big data filling method based on tensor train decomposition model

A filling method and tensor model technology, applied in the field of transportation, can solve problems such as large amount of calculation, unstable data decomposition, unsuitable high-dimensional data decomposition, etc., and achieve the effect of maintaining filling stability and improving accuracy

Active Publication Date: 2020-06-19
SOUTHEAST UNIV
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Problems solved by technology

Since the CP decomposition model cannot represent the data well, the decomposition is unstable, and the Tucker decomposition is computationally intensive in the application of high-dimensional data, which is not suitable for the decomposition of high-dimensional data. Now the tensor train decomposition model (TT decomposition) is being developed Research

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  • Traffic big data filling method based on tensor train decomposition model
  • Traffic big data filling method based on tensor train decomposition model
  • Traffic big data filling method based on tensor train decomposition model

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Embodiment Construction

[0069] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0070] As shown in the figure, a traffic big data filling method based on the tensor train decomposition model described in the present invention uses L2 regularization and trace norm regularization to perform constrained optimization on the nuclear tensor, and characterizes the original tensor in this way, Implements estimated padding of missing data for raw tensors. When solving, the design derives different optimal solution methods. The first optimization method aims at algorithm acceleration, relaxes the constraints on the model, and introduces the conjugate gradient method to optimize the solution, using the step convergence of the conjugate gradient method to qu...

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Abstract

The invention discloses a traffic big data filling method based on a tensor train decomposition model. The method comprises the following steps: constructing a five-dimensional tensor model containingfive traffic data dimensions; constructing an initial filling model based on a tensor train decomposition model through L2 regular constraint; performing conjugate gradient optimization on the filling model to obtain an optimized filling model of each kernel vector; or performing trace norm optimization on the filling model to obtain a final filling model; and performing traffic big data fillingthrough the first filling model and / or the second filling model. According to the method provided by the invention, the data filling precision can be improved, and the filling stability can be maintained at a high loss rate.

Description

technical field [0001] The invention belongs to the field of transportation, and in particular relates to a method for filling traffic big data based on tensor train decomposition. Background technique [0002] Since the rapid development of big data, the acquisition of massive data has brought great opportunities and challenges to the field of transportation. The combination of modern computer technology and traditional transportation technology has given birth to a series of intelligent transportation industries. In 2017, " The model of "Internet + transportation" has sprung up in major cities. However, the big data industry needs to complete healthy data as a support. In real life, due to the failure of detection equipment and transmission equipment or the influence of bad weather, there are different degrees of loss in the original traffic data. And the adverse effects of deep excavation. [0003] Tensor is a natural expression of high-dimensional data, especially suit...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16
CPCG06F17/16Y02T10/40
Inventor 谭华春丁璠王梵晔蒋竺希伍元凯李琴
Owner SOUTHEAST UNIV
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