Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model

2019 ◽  
Vol 104 ◽  
pp. 66-77 ◽  
Author(s):  
Xinyu Chen ◽  
Zhaocheng He ◽  
Yixian Chen ◽  
Yuhuan Lu ◽  
Jiawei Wang
Keyword(s):  
Pattern Discovery ◽  
Data Imputation ◽  
Traffic Data ◽  
Tensor Factorization ◽  
Factorization Model

scholarly journals Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yaxiong Han ◽  
Zhaocheng He
Keyword(s):  
Dirichlet Process ◽  
Traffic Management ◽  
Pattern Discovery ◽  
Tensor Decomposition ◽  
Data Imputation ◽  
Traffic Data ◽  
Bayesian Nonparametric ◽  
Dirichlet Process Mixture ◽  
Wide Range ◽  
Similarity Pattern

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data.

scholarly journals Enhanced tensor factorization framework using non-negative and probabilistic tensor factorization approaches for microblogging content propagation modelling

2017 ◽  
Vol 7 (1.1) ◽  
pp. 88
Author(s):  
N. Baggyalakshmi ◽  
A. Kavitha ◽  
A. Marimuthu
Keyword(s):  
Gradient Descent ◽  
User Behavior ◽  
Complete System ◽  
Propagation Model ◽  
Tensor Factorization ◽  
Propagation Modeling ◽  
Factorization Model ◽  
Factorization Structure ◽  
Linguistic Patterns ◽  
Fast Gradient

With the aim of identifying the user’s preferences, Content propagation modeling from the micro-blogging sites aids diverse organizations. In existing studies, four user behavior aspects were used by the content propagation model that is to say topic virality, user’s position, user susceptibility and user virality. The propagation occurrences are signified as a tensor factorization model so-called V2S is presented with the aim of deriving the behavioral aspects via which the content propagation is designed. On the other hand, it doesn’t comprise the linguistic patterns in the content that decreases the performance of the content propagation. Moreover, by utilizing advanced tensor approaches, the factorization structure is improved. Therefore the performance of the complete system is decreased meaningfully. With the aim of overcoming the aforesaid problems, Enhanced V2S (EV2S) Tensor Factorization framework is presented in this research that make use of the Probabilistic Latent Tensor Factorization (PLTF) as well as Non-negative Tensor Factorization (NTF) in order to derive the behavioral facets. NTF is presented for decreasing the content propagation errors. By making use of fast gradient descent technique, the unrestrained issue, which happens in this model is solved. This research system identifies the reposts as well as re-tweets in huge datasets proficiently with minimum processing time. From the experimentation outcomes, it is proved that the EV2S-PLTF tensor factorization performs better when compared to the previous tensor frameworks.

Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition

2021 ◽  
pp. 2150027
Author(s):  
Junlan Nie ◽  
Ruibo Gao ◽  
Ye Kang
Keyword(s):  
Noise Pollution ◽  
Tensor Decomposition ◽  
Prediction Algorithm ◽  
Kernel Matrix ◽  
Multiple Views ◽  
Tensor Factorization ◽  
Urban Noise ◽  
Model Based ◽  
Factorization Model ◽  
Kernel Distance

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems

2020 ◽  
Vol 209 ◽  
pp. 106434
Author(s):  
Jianli Zhao ◽  
Wei Wang ◽  
Zipei Zhang ◽  
Qiuxia Sun ◽  
Huan Huo ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization ◽  
Factorization Model ◽  
User Trust
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