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Urban Traffic State Estimation with Online Car-Hailing Data: A Dynamic Tensor-Based Bayesian Probabilistic Decomposition Approach
2022
Journal of Advanced Transportation
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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
2020
Conference on Uncertainty in Artificial Intelligence
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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]
2020
arXiv
pre-print
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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]
2016
Lecture Notes in Computer Science
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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
2016
Knowledge-Based Systems
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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]
2012
arXiv
pre-print
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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
2021
IEEE Journal on Selected Topics in Signal Processing
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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)
2016
Dagstuhl Reports
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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]
2018
arXiv
pre-print
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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
2009
Journal of machine learning research
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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
2015
2015 23rd European Signal Processing Conference (EUSIPCO)
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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
2008
Computational Intelligence and Neuroscience
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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
2008
Computer
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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
2012
Data mining and knowledge discovery
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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]
2021
arXiv
pre-print
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2019 pre-print arXiv:1904.10951v1 fatcat:mzt3jqa6y5bhlpagver6hruqku
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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