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Urban Traffic State Estimation with Online Car-Hailing Data: A Dynamic Tensor-Based Bayesian Probabilistic Decomposition Approach

Wenqi Lu, Ziwei Yi, Dongyu Luo, Yikang Rui, Bin Ran, Jianqing Wu, Tao Li
2022 Journal of Advanced Transportation  
Considering the benefit of the tensor decomposition, we present a novel urban traffic state estimation based on dynamic tensor and Bayesian probabilistic decomposition.  ...  Then, a dynamic tensor Bayesian probabilistic decomposition (DTBPD) approach is built by decomposing the dynamic tensor into the outer product of several vectors.  ...  Tensor-Based Bayesian Probabilistic CP Decomposition. is section introduces the Bayesian probabilistic CP decomposition utilized to produce the predicted value in a dynamic tensor 􏽥 X.  ... 
doi:10.1155/2022/1793060 doaj:bbf3dce58ddb4428bad219401597be8c fatcat:3u2ruynlifbxxe3ngchek6ansa

Streaming Nonlinear Bayesian Tensor Decomposition

Zhimeng Pan, Zheng Wang, Shandian Zhe
2020 Conference on Uncertainty in Artificial Intelligence  
To address this problem, we propose Streaming Nonlinear Bayesian Tensor Decomposition (SNBTD) that can conduct high-quality, closedform and iteration-free updates upon receiving new tensor entries.  ...  Despite the success of the recent nonlinear tensor decomposition models based on Gaussian processes (GPs), they lack an effective way to deal with streaming data, which are important for many applications  ...  We compared with the following baselines. (1) POST (Du et al., 2018) , the state-ofthe-art streaming tensor decomposition algorithm based on a probabilistic CP model.  ... 
dblp:conf/uai/PanWZ20 fatcat:34r4h3gaurhnlpjiq4peop3l4y

Streaming Probabilistic Deep Tensor Factorization [article]

Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe
2020 arXiv   pre-print
To address these issues, we propose SPIDER, a Streaming ProbabilistIc Deep tEnsoR factorization method. We first use Bayesian neural networks (NNs) to construct a deep tensor factorization model.  ...  Despite the success of existing tensor factorization methods, most of them conduct a multilinear decomposition, and rarely exploit powerful modeling frameworks, like deep neural networks, to capture a  ...  We compared with the following baselines. (1) POST (Du et al., 2018) , the state-of-the-art streaming tensor decomposition algorithm based on a probabilistic CP model.  ... 
arXiv:2007.07367v1 fatcat:7cernqt3onbybaf2wn7zfxt7uu

Factorization of Multiple Tensors for Supervised Feature Extraction [chapter]

Wei Liu
2016 Lecture Notes in Computer Science  
To tackle this problem, we design a novel probabilistic tensor factorization model that takes both features and class labels of tensors into account, and produces informative common and unique factors  ...  among the tensors: 1) if each tensor is factorized separately, the factor matrices will fail to explicitly capture the common information shared by different tensors, and 2) if tensors are concatenated  ...  We formulate this problem into the task of discovering common and unique factors from multiple tensors.  ... 
doi:10.1007/978-3-319-46675-0_44 fatcat:xcqp3jbctzev3o4rk2systklja

Tensor-based anomaly detection: An interdisciplinary survey

Hadi Fanaee-T, João Gama
2016 Knowledge-Based Systems  
However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods.  ...  This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures.  ...  There exist some streaming approximation solutions for this problem for either classical tensor decomposition, subspace analysis or probabilistic tensor decomposition.  ... 
doi:10.1016/j.knosys.2016.01.027 fatcat:lejxxae63jcutfx2ncahownt7e

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis [article]

Zenglin Xu, Feng Yan, Yuan Qi
2012 arXiv   pre-print
Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework.  ...  Tensor decomposition is a powerful computational tool for multiway data analysis.  ...  (WCP) and Probabilistic Tucker Decomposition (PTD).  ... 
arXiv:1108.6296v2 fatcat:5lmkluhoundudjt63a6mnr6jne

Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning

Hongyang Chen, Sergiy A. Vorobyov, Hing Cheung So, Fauzia Ahmad, Fatih Porikli
2021 IEEE Journal on Selected Topics in Signal Processing  
Chachlakis et al. propose a novel framework for streaming and dynamic Tucker tensor decomposition, based on maximum L1-norm projection.  ...  BAM covers various probabilistic nonnegative tensor factorization (NTF) and topic models under one general framework.  ... 
doi:10.1109/jstsp.2021.3065184 fatcat:qbvihejwkfaa5hoztety77pnwi

Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152)

Avrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem Rusitschka, Volker Tresp, Marc Herbstritt
2016 Dagstuhl Reports  
This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 16152 "Tensor Computing for Internet of Things".  ...  On the other hand, IoT/CPS have characteristics that make tensor methods applicable to extract information very efficiently.  ...  of parameters of the probabilistic models.  ... 
doi:10.4230/dagrep.6.4.57 dblp:journals/dagstuhl-reports/AcarAMRT16 fatcat:psomri4q4faapbjc4dot7ca3em

Tensor Completion Algorithms in Big Data Analytics [article]

Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu
2018 arXiv   pre-print
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.  ...  We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and  ...  Streaming Tensor Completion Versus Multi-Aspect Steaming Tensor Completion(le ), Multi-Aspect Streaming Tensor Decomposition (right). [179] Table 1 . 1 Main symbols and operations.  ... 
arXiv:1711.10105v2 fatcat:onu2bket3na3dmfsmzhc7byqku

Probabilistic Models for Incomplete Multi-dimensional Arrays

Wei Chu, Zoubin Ghahramani
2009 Journal of machine learning research  
We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker.  ...  Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets.  ...  Sun et al. (2006) proposed an incremental algorithm for tensor dimensionality reduction, known as dynamic tensor analysis, which is scalable for semi-infinite streams.  ... 
dblp:journals/jmlr/ChuG09 fatcat:fq55coqk2zgxfkl3zxmuxhprj4

Online sketching for big data subspace learning

Morteza Mardani, Georgios B. Giannakis
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
To cope with these challenges, the present paper brings forth a novel real-time sketching scheme that exploits the correlations across data stream to learn a latent subspace based upon tensor PARAFAC decomposition  ...  Focusing on three-way tensors, seen as a stream of correlated slices (matrices), PARAFAC decomposition is adapted to effect low rank for the latent subspace.  ...  When the decomposition is exact, this offers the PARAFAC decomposition of X.  ... 
doi:10.1109/eusipco.2015.7362837 dblp:conf/eusipco/MardaniG15 fatcat:mt2plztsvfebrajmtaornol2ou

Advances in Nonnegative Matrix and Tensor Factorization

A. Cichocki, M. Mørup, P. Smaragdis, W. Wang, R. Zdunek
2008 Computational Intelligence and Neuroscience  
The seventh paper, entitled "Single-trial decoding of bistable perception based on sparse nonnegative tensor decomposition" by Z.  ...  The first paper, entitled "Probabilistic latent variable models as nonnegative factorizations" by M.  ... 
doi:10.1155/2008/852187 pmid:18615193 pmcid:PMC2443422 fatcat:bbjdwfrcfzf57bulhukdilqa2i

Noninvasive BCIs: Multiway Signal-Processing Array Decompositions

A. Cichocki, Y. Washizawa, T. Rutkowski, H. Bakardjian, Anh-Huy Phan, Seungjin Choi, Hyekyoung Lee, Qibin Zhao, Liqing Zhang, Yuanqing Li
2008 Computer  
of dynamic streams (sequence) of tensors.  ...  Moreover, results can be given a probabilistic interpretation.  ... 
doi:10.1109/mc.2008.431 fatcat:auvhydbdcnagdehqhjbmxxgnnm

Behavioral event data and their analysis

Ian Davidson, Sean Gilpin, Peter B. Walker
2012 Data mining and knowledge discovery  
Though the data naturally lends itself to be represented as graphs and tensors we show how existing techniques are limited in their usefulness and outline our own algorithms to overcome these challenges  ...  We also investigated a probabilistic interpretation to the tensor decomposition that allows us to make unstructured predictions.  ...  We introduce a probabilistic generative model interpretation of tensor decomposition which allows us to make these unstructured predictions (Sect. 5). • We show how to represent behavioral event data as  ... 
doi:10.1007/s10618-012-0269-7 fatcat:llllwe35gzacvkoroxo2gznwci

Low-Rank Tucker Approximation of a Tensor From Streaming Data [article]

Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, Madeleine Udell
2021 arXiv   pre-print
Extensive numerical experiments show that that the algorithm produces useful results that improve on the state of the art for streaming Tucker decomposition.  ...  The sketch can be extracted from streaming or distributed data or with a single pass over the tensor, and it uses storage proportional to the degrees of freedom in the output Tucker approximation.  ...  Probabilistic Core Error Bound. In this section, we derive a probabilistic error bound based on the core error decomposition from Lemma C.1. Lemma C.2.  ... 
arXiv:1904.10951v2 fatcat:j7fiumlb6vb4pb3pspr4ldpypm
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