Abstract
Due to the accelerated development and popularization of Internet, mobile Internet, and Internet of Things and the breakthrough of storage and communication technologies, the amount of data obtained in the fields of health care, social media, and climate science is increasing, showing complex high-dimensional, multimodal, and heterogeneous characteristics. As the expansion of a vector and matrix, a tensor is the natural and essential mode of representation for this kind of data. The theory of tensor algebra provides a powerful mathematical tool and an extensible framework for learning algorithms for processing data with high-dimensional heterogeneity and complex dependence. In recent years, tensor theory and its applications have become a research hotspot, from new tensor models and scalable algorithms in academia to industry solutions. The article shows its advances in tensor theories, algorithms, and applications. Firstly, tensor operation, classical tensor decomposition theory, and t-product tensor theory are introduced. Secondly, tensor supervised learning, tensor unsupervised learning, and tensor deep learning are discussed from the perspective of tensor decomposition and t-product, and then their application research is summarized. Finally, the opportunities and challenges of tensor learning are briefly discussed.
Similar content being viewed by others
References
Tao D, Li X, Hu W, Maybank S, Wu X (2005) Supervised tensor learning. In: Fifth IEEE international conference on data mining (ICDM’05), IEEE, p 8
Ben X, Zhang P, Lai Z, Yan R, Zhai X, Meng W (2019) A general tensor representation framework for cross-view gait recognition. Pattern Recogn 90:87–98
Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51 (3):455–500
Comon P (2014) Tensors: a brief introduction. IEEE Signal Proc Mag 31(3):44–53
Papalexakis EE, Faloutsos C, Sidiropoulos ND (2016) Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans Intell Syst Technol (TIST) 8(2):1–44
Bro R (1997) Parafac. tutorial and applications. Chemometr Intell Lab Syst 38(2):149–171
Cichocki A, Mandic D, De Lathauwer L, Zhou G, Zhao Q, Caiafa C, Phan HA (2015) Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process Mag 32(2):145–163
Sidiropoulos ND, De Lathauwer L, Fu X, Huang K, Papalexakis EE, Faloutsos C (2017) Tensor decomposition for signal processing and machine learning. IEEE Trans Signal Process 65(13):3551–3582
De Silva V, Lim L-H (2008) Tensor rank and the ill-posedness of the best low-rank approximation problem. SIAM J Matrix Anal Appl 30(3):1084–1127
Oseledets IV (2011) Tensor-train decomposition. SIAM J Sci Comput 33(5):2295–2317
Hitchcock FL (1927) The expression of a tensor or a polyadic as a sum of products. J Math Phys 6(1-4):164–189
Hitchcock FL (1928) Multiple invariants and generalized rank of a p-way matrix or tensor. J Math Phys 7(1-4):39–79
Cichocki A, Lee N, Oseledets I, Phan A-H, Zhao Q, Mandic DP et al (2016) Tensor networks for dimensionality reduction and large-scale optimization: part 1 low-rank tensor decompositions. Found Trends Mach Learn 9(4-5):249–429
Zhao Q, Zhou G, Xie S, Zhang L, Cichocki A (2016) Tensor ring decomposition. arXiv:1606.05535
Kilmer ME, Martin CD (2011) Factorization strategies for third-order tensors. Linear Algebra Appl 435(3):641–658
Kilmer ME, Braman K, Hao N, Hoover RC (2013) Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J Matrix Anal Appl 34 (1):148–172
Zhou H, Li L, Zhu H (2013) Tensor regression with applications in neuroimaging data analysis. J Am Stat Assoc 108(502):540–552
Hoff PD (2015) Multilinear tensor regression for longitudinal relational data. Ann Appl Stat 9 (3):1169
Yu R, Liu Y (2016) Learning from multiway data: simple and efficient tensor regression. In: International conference on machine learning, PMLR, pp 373–381
Rabusseau G, Kadri H (2016) Low-rank regression with tensor responses. Adv Neural Inf Process Syst 29
Sun WW, Li L (2017) Store: sparse tensor response regression and neuroimaging analysis. J Mach Learn Res 18(1):4908–4944
Liu J, Zhu C, Long Z, Huang H, Liu Y (2021) Low-rank tensor ring learning for multi-linear regression. Pattern Recogn 113:107753
Wang D, Zheng Y, Lian H, Li G (2022) High-dimensional vector autoregressive time series modeling via tensor decomposition. J Am Stat Assoc 117(539):1338–1356
Li C, Zhang H (2021) Tensor quantile regression with application to association between neuroimages and human intelligence. Ann Appl Stat 15(3):1455–1477
Zhao Q, Zhou G, Adali T, Zhang L, Cichocki A (2013) Kernelization of tensor-based models for multiway data analysis: processing of multidimensional structured data. IEEE Signal Proc Mag 30 (4):137–148
Hao B, Wang B, Wang P, Zhang J, Yang J, Sun WW (2021) Sparse tensor additive regression. J Mach Learn Res 22
Huang J, Horowitz JL, Wei F (2010) Variable selection in nonparametric additive models. Ann Stat 38(4):2282–2313
Fan J, Feng Y, Song R (2011) Nonparametric independence screening in sparse ultra-high-dimensional additive models. J Am Stat Assoc 106(494):544–557
Luo L, Xie Y, Zhang Z, Li W-J (2015) Support matrix machines. In: International conference on machine learning, PMLR, pp 938–947
Luo L, Xie Y, Zhang Z, Li W-J (2015) Support matrix machines. In: International conference on machine learning, PMLR, pp 938–947
Cai D, He X, Han J (2006) Learning with tensor representation. Technical Report
Tao D, Li X, Hu W, Maybank S, Wu X (2005) Supervised tensor learning. In: Fifth IEEE international conference on data mining (ICDM’05), IEEE, p 8
Kotsia I, Patras I (2011) Support tucker machines. In: CVPR 2011, IEEE, pp 633–640
Kotsia I, Guo W, Patras I (2012) Higher rank support tensor machines for visual recognition. Pattern Recogn 45(12):4192–4203
Hao Z, He L, Chen B, Yang X (2013) A linear support higher-order tensor machine for classification. IEEE Trans Image Process 22(7):2911–2920
Chen C, Batselier K, Ko C-Y, Wong N (2019) A support tensor train machine. In: 2019 International joint conference on neural networks (IJCNN), IEEE, pp 1–8
Signoretto M, Olivetti E, De Lathauwer L, Suykens JA (2012) Classification of multichannel signals with cumulant-based kernels. IEEE Trans Signal Process 60(5):2304–2314
Zhao Q, Zhou G, Adalı T, Zhang L, Cichocki A (2013) Kernel-based tensor partial least squares for reconstruction of limb movements. In: 2013 IEEE International conference on acoustics, speech and signal processing, IEEE, pp 3577–3581
He L, Kong X, Yu PS, Yang X, Ragin AB, Hao Z (2014) Dusk: a dual structure-preserving kernel for supervised tensor learning with applications to neuroimages. In: Proceedings of the 2014 SIAM international conference on data mining, SIAM, pp 127–135
He L, Lu C-T, Ding H, Wang S, Shen L, Yu PS, Ragin AB (2017) Multi-way multi-level kernel modeling for neuroimaging classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 356–364
Chen C, Batselier K, Yu W, Wong N (2022) Kernelized support tensor train machines. Pattern Recogn 122:108337
Friedland S, Li Q, Schonfeld D (2014) Compressive sensing of sparse tensors. IEEE Trans Image Process 23(10):4438–4447
Boche H, Calderbank R, Kutyniok G, Vybiral J et al (2015) Compressed sensing and its applications. In: Boche H, Caire G, Calderbank R, Marz M, Kutynick G, Mathar R (eds) Compressed sensing and its applications, Springer, 2017, pp 1–54
Bernal EA, Li Q (2015) Hybrid vectorial and tensorial compressive sensing for hyperspectral imaging. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 2454–2458
Li Q, Bernal EA (2016) Hybrid tenso-vectorial compressive sensing for hyperspectral imaging. J Electr Imag 25(3):033001
Sun WW, Li L (2019) Dynamic tensor clustering. J Am Stat Assoc 114(528):1894–1907
Wu J, Lin Z, Zha H (2019) Essential tensor learning for multi-view spectral clustering. IEEE Trans Image Process 28(12):5910–5922
Yin M, Gao J, Xie S, Guo Y (2018) Multiview subspace clustering via tensorial t-product representation. IEEE Trans Neural Netw Learn Syst 30(3):851–864
Sun W, Wang Z, Liu H, Cheng G (2015) Non-convex statistical optimization for sparse tensor graphical model. Adv Neural Inf Process Syst 28
Lyu X, Sun WW, Wang Z, Liu H, Yang J, Cheng G (2019) Tensor graphical model: non-convex optimization and statistical inference. IEEE Trans Pattern Anal Mach Intell 42(8):2024–2037
He S, Yin J, Li H, Wang X (2014) Graphical model selection and estimation for high dimensional tensor data. J Multivar Anal 128:165–185
Shahid N, Grassi F, Vandergheynst P (2016) Multilinear low-rank tensors on graphs & applications. arXiv:1611.04835
Xu P, Zhang T, Gu Q (2017) Efficient algorithm for sparse tensor-variate gaussian graphical models via gradient descent. In: Artificial intelligence and statistics, PMLR, pp 923–932
Li Y, Fujita H, Li J, Liu C, Zhang Z (2022) Tensor approximate entropy: an entropy measure for sleep scoring. Knowl-Based Syst 245:108503
Du S, Shi Y, Shan G, Wang W, Ma Y (2021) Tensor low-rank sparse representation for tensor subspace learning. Neurocomputing 440:351–364
Du S, Liu B, Shan G, Shi Y, Wang W (2022) Enhanced tensor low-rank representation for clustering and denoising. Knowl-Based Syst 243:108468
Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. Adv Neural Inf Process Syst 27
Lebedev V, Ganin Y, Rakhuba M, Oseledets I, Lempitsky V (2014) Speeding-up convolutional neural networks using fine-tuned cp-decomposition. arXiv:1412.6553
Tai C, Xiao T, Zhang Y, Wang X et al (2015) Convolutional neural networks with low-rank regularization. arXiv:1511.06067
Kim Y-D, Park E, Yoo S, Choi T, Yang L, Shin D (2015) Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv:1511.06530
Kossaifi J, Toisoul A, Bulat A, Panagakis Y, Hospedales TM, Pantic M (2020) Factorized higher-order cnns with an application to spatio-temporal emotion estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6060–6069
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Novikov A, Podoprikhin D, Osokin A, Vetrov DP (2015) Tensorizing neural networks. Adv Neural Inf Process Syst 28
Ye J, Li G, Chen D, Yang H, Zhe S, Xu Z (2020) Block-term tensor neural networks. Neural Netw 130:11–21
Kossaifi J, Lipton ZC, Kolbeinsson A, Khanna A, Furlanello T, Anandkumar A (2020) Tensor regression networks. J Mach Learn Res 21(1):4862–4882
Kasiviswanathan SP, Narodytska N, Jin H (2018) Network approximation using tensor sketching. In: IJCAI, pp 2319–2325
Kossaifi J, Bulat A, Tzimiropoulos G, Pantic M (2019) T-net: parametrizing fully convolutional nets with a single high-order tensor. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7822–7831
Yang Y, Krompass D, Tresp V (2017) Tensor-train recurrent neural networks for video classification. In: International conference on machine learning, PMLR, pp 3891–3900
Giampouras PV, Rontogiannis AA, Kofidis E (2022) Block-term tensor decomposition model selection and computation: The bayesian way. IEEE Trans Signal Process 70:1704–1717
Ye J, Wang L, Li G, Chen D, Zhe S, Chu X, Xu Z (2018) Learning compact recurrent neural networks with block-term tensor decomposition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 9378–9387
Khrulkov V, Hrinchuk O, Oseledets I (2019) Generalized tensor models for recurrent neural networks. arXiv:1901.10801
Cohen N, Sharir O, Shashua A (2016) On the expressive power of deep learning: a tensor analysis. In: Conference on learning theory, PMLR, pp 698–728
Sharir O, Shashua A (2017) On the expressive power of overlapping architectures of deep learning. arXiv:1703.02065
Khrulkov V, Novikov A, Oseledets I (2017) Expressive power of recurrent neural networks. arXiv:1711.00811
Li J, Sun Y, Su J, Suzuki T, Huang F (2020) Understanding generalization in deep learning via tensor methods. In: International conference on artificial intelligence and statistics, PMLR, pp 504–515
Janzamin M, Sedghi H, Anandkumar A (2015) Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods. arXiv:1506.08473
Ge R, Lee JD, Ma T (2017) Learning one-hidden-layer neural networks with landscape design. arXiv:1711.00501
Mondelli M, Montanari A (2019) On the connection between learning two-layer neural networks and tensor decomposition. In: The 22nd International conference on artificial intelligence and statistics, PMLR, pp 1051–1060
Newman E, Horesh L, Avron H, Kilmer M (2018) Stable tensor neural networks for rapid deep learning. arXiv:1811.06569
Yin M, Gao J, Xie S, Guo Y (2018) Multiview subspace clustering via tensorial t-product representation. IEEE Trans Neural Netw Learn Syst 30(3):851–864
Bibi A, Ghanem B (2017) High order tensor formulation for convolutional sparse coding. In: Proceedings of the IEEE international conference on computer vision, pp 1772–1780
Lu C, Feng J, Chen Y, Liu W, Lin Z, Yan S (2019) Tensor robust principal component analysis with a new tensor nuclear norm. IEEE Trans Pattern Anal Mach Intell 42(4):925–938
Wang X, Che M, Wei Y (2020) Tensor neural network models for tensor singular value decompositions. Comput Optim Appl 75(3):753–777
Zhou Y, Cheung Y-M (2019) Bayesian low-tubal-rank robust tensor factorization with multi-rank determination. IEEE Trans Pattern Anal Mach Intell 43(1):62–76
He H, Ling C, Xie W (2022) Tensor completion via a generalized transformed tensor t-product decomposition without t-svd. J Sci Comput 93(2):1–35
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Biswas SK, Milanfar P (2017) Linear support tensor machine with lsk channels: pedestrian detection in thermal infrared images. IEEE Trans Image Process 26(9):4229–4242
Chen Z, Batselier K, Suykens JA, Wong N (2017) Parallelized tensor train learning of polynomial classifiers. IEEE Trans Neural Netw Learn Syst 29(10):4621–4632
Afshar A, Yin K, Yan S, Qian C, Ho J, Park H, Sun J (2021) Swift: scalable wasserstein factorization for sparse nonnegative tensors. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 6548–6556
Ho JC, Ghosh J, Sun J (2014) Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 115–124
Ho JC, Ghosh J, Steinhubl SR, Stewart WF, Denny JC, Malin BA, Sun J (2014) Limestone: High-throughput candidate phenotype generation via tensor factorization. J Biomed Inform 52:199–211
Li Y, Ngom A (2010) Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data. In: 2010 IEEE International conference on bioinformatics and biomedicine (BIBM), IEEE, pp 438–443
Fanaee-T H, Gama J (2014) An eigenvector-based hotspot detection. arXiv:1406.3191
Wang Y, Chen R, Ghosh J, Denny JC, Kho A, Chen Y, Malin BA, Sun J (2015) Rubik: Knowledge guided tensor factorization and completion for health data analytics. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1265–1274
Mu Y, Ding W, Morabito M, Tao D (2011) Empirical discriminative tensor analysis for crime forecasting. In: International conference on knowledge science, engineering and management, Springer, pp 293–304
Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 25–34
Zheng Y, Liu T, Wang Y, Zhu Y, Liu Y, Chang E (2014) Diagnosing new york city’s noises with ubiquitous data. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, pp 715–725
Kuang L, Yang LT, Qiu K (2016) Tensor-based software-defined internet of things. IEEE Wirel Commun 23(5):84–89
Gao Y, Zhang G, Zhang C, Wang J, Yang LT, Zhao Y (2021) Federated tensor decomposition-based feature extraction approach for industrial iot. IEEE Trans Ind Inf 17(12):8541–8549
Singh A, Aujla GS, Garg S, Kaddoum G, Singh G (2019) Deep-learning-based sdn model for internet of things: an incremental tensor train approach. IEEE Internet Things J 7(7):6302–6311
Liu H, Yang LT, Lin M, Yin D, Guo Y (2018) A tensor-based holistic edge computing optimization framework for internet of things. IEEE Netw 32(1):88–95
Liu H, Yang LT, Ding J, Guo Y, Xie X, Wang Z-J (2020) Scalable tensor-train-based tensor computations for cyber–physical–social big data. IEEE Trans Comput Soc Syst 7(4):873–885
Wang W, Zhang M (2018) Tensor deep learning model for heterogeneous data fusion in internet of things. IEEE Trans Emerg Top Comput Intell 4(1):32–41
Li P, Chen Z, Yang LT, Zhang Q, Deen MJ (2017) Deep convolutional computation model for feature learning on big data in internet of things. IEEE Trans Ind Inf 14(2):790–798
Deng X, Jiang P, Peng X, Mi C (2018) An intelligent outlier detection method with one class support tucker machine and genetic algorithm toward big sensor data in internet of things. IEEE Trans Ind Electron 66(6):4672–4683
Cheng Y, Li G, Wong N, Chen H-B, Yu H (2020) Deepeye: a deeply tensor-compressed neural network for video comprehension on terminal devices. ACM Trans Embed Comput Syst (TECS) 19 (3):1–25
Liang J, Yu G, Chen B, Zhao M (2015) Decentralized dimensionality reduction for distributed tensor data across sensor networks. IEEE Trans Neural Netw Learn Syst 27(11):2174–2186
He J, Zhou Y, Sun G, Geng T (2019) Multi-attribute data recovery in wireless sensor networks with joint sparsity and low-rank constraints based on tensor completion. IEEE Access 7:135220–135230
Renard N, Bourennane S (2008) Improvement of target detection methods by multiway filtering. IEEE Trans Geosci Remote Sens 46(8):2407–2417
Makantasis K, Doulamis A, Doulamis N, Nikitakis A (2017) Tensor-based classifiers for hyperspectral data analysis. arXiv:1709.08164
Renard N, Bourennane S (2009) Dimensionality reduction based on tensor modeling for classification methods. IEEE Trans Geosci Remote Sens 47(4):1123–1131
Zhang Q, Wang H, Plemmons RJ, Pauca VP (2008) Tensor methods for hyperspectral data analysis: a space object material identification study. JOSA A 25(12):3001–3012
Zhang L, Zhang L, Tao D, Huang X (2010) A multifeature tensor for remote-sensing target recognition. IEEE Geosci Remote Sens Lett 8(2):374–378
Guo X, Huang X, Zhang L, Zhang L, Plaza A, Benediktsson JA (2016) Support tensor machines for classification of hyperspectral remote sensing imagery. IEEE Trans Geosci Remote Sens 54(6):3248–3264
Lu H, Plataniotis KN, Venetsanopoulos AN (2008) Mpca: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39
Xiong L, Chen X, Huang T-K, Schneider J, Carbonell JG (2010) Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of the 2010 SIAM international conference on data mining, SIAM, pp 211–222
Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th international conference on machine learning, pp 880–887
Karatzoglou A, Amatriain X, Baltrunas L, Oliver N (2010) Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM conference on recommender systems, pp 79– 86
Rendle S (2010) Factorization machines. In: 2010 IEEE international conference on data mining, IEEE, pp 995–1000
Zhu Z, Hu X, Caverlee J (2018) Fairness-aware tensor-based recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1153–1162
Shan Y, Hoens TR, Jiao J, Wang H, Yu D, Mao J (2016) Deep crossing: Web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 255–262
Cohn D, Hofmann T (2000) The missing link-a probabilistic model of document content and hypertext connectivity. Adv Neural Inf Process Syst 13
Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM conference on recommender systems, pp 43–50
Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM international conference on web search and data mining, pp 81–90
Ran B, Tan H, Wu Y, Jin PJ (2016) Tensor based missing traffic data completion with spatial–temporal correlation. Physica A Stat Mech Appl 446:54–63
Zhang H, Chen P, Zheng J, Zhu J, Yu G, Wang Y, Liu HX (2019) Missing data detection and imputation for urban anpr system using an iterative tensor decomposition approach. Trans Res Part C Emerg Technol 107:337–355
Tan H, Wu Y, Shen B, Jin PJ, Ran B (2016) Short-term traffic prediction based on dynamic tensor completion. IEEE Trans Intell Transp Syst 17(8):2123–2133
Tan H, Wu Y, Shen B, Jin PJ, Ran B (2016) Short-term traffic prediction based on dynamic tensor completion. IEEE Trans Intell Transp Syst 17(8):2123–2133
Chen X, Chen Y, Saunier N, Sun L (2021) Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transp Res Part C Emerg Technol 129:103226
Wang J, Gao F, Cui P, Li C, Xiong Z (2014) Discovering urban spatio-temporal structure from time-evolving traffic networks. In: Asia-pacific web conference, Springer, pp 93–104
Fanaee-T H, Gama J (2016) Event detection from traffic tensors: a hybrid model. Neurocomputing 203:22–33
Tan H, Feng J, Feng G, Wang W, Zhang Y-J (2013) Traffic volume data outlier recovery via tensor model. Math Probl Eng 2013
Tan H, Feng G, Feng J, Wang W, Zhang Y-J, Li F (2013) A tensor-based method for missing traffic data completion. Trans Res Part C Emerg Technol 28:15–27
Acar E, Aykut-Bingol C, Bingol H, Bro R, Yener B (2007) Multiway analysis of epilepsy tensors. Bioinformatics 23(13):10–18
Papalexakis EE, Faloutsos C, Mitchell TM, Talukdar PP, Sidiropoulos ND, Murphy B (2014) Turbo-smt: accelerating coupled sparse matrix-tensor factorizations by 200x. In: Proceedings of the 2014 SIAM international conference on data mining, SIAM, pp 118–126
Chen D, Li X, Wang L, Khan SU, Wang J, Zeng K, Cai C (2014) Fast and scalable multi-way analysis of massive neural data. IEEE Trans Comput 64(3):707–719
Dao NTA, Dung NV, Trung NL, Abed-Meraim K et al (2020) Multi-channel eeg epileptic spike detection by a new method of tensor decomposition. J Neural Eng 17(1):016023
Duan F, Jia H, Zhang Z, Feng F, Tan Y, Dai Y, Cichocki A, Yang Z, Caiafa CF, Sun Z et al (2021) On the robustness of eeg tensor completion methods. Sci China Technol Sci 64 (9):1828–1842
Nion D, Sidiropoulos ND (2010) Tensor algebra and multidimensional harmonic retrieval in signal processing for mimo radar. IEEE Trans Signal Process 58(11):5693–5705
Muti D, Bourennane S (2005) Multidimensional filtering based on a tensor approach. Signal Process 85(12):2338–2353
Stanley JS, Chi EC, Mishne G (2020) Multiway graph signal processing on tensors: Integrative analysis of irregular geometries. IEEE Signal Proc Mag 37(6):160–173
Han K, Nehorai A (2014) Nested vector-sensor array processing via tensor modeling. IEEE Trans Signal Process 62(10):2542–2553
De Lathauwer L, Castaing J (2007) Tensor-based techniques for the blind separation of ds–cdma signals. Signal Process 87(2):322–336
De Lathauwer L (1997) Signal processing based on multilinear algebra katholieke universiteit leuven leuven
Wang X, Wang W, Yang LT, Liao S, Yin D, Deen MJ (2018) A distributed hosvd method with its incremental computation for big data in cyber-physical-social systems. IEEE Trans Comput Soc Syst 5(2):481–492
Wang X, Yang LT, Chen X, Wang L, Ranjan R, Chen X, Deen MJ (2018) A multi-order distributed hosvd with its incremental computing for big services in cyber-physical-social systems. IEEE Trans Big Data 6(4):666–678
Bu F (2017) A high-order clustering algorithm based on dropout deep learning for heterogeneous data in cyber-physical-social systems. IEEE Access 6:11687–11693
Zhang S, Yang LT, Feng J, Wei W, Cui Z, Xie X, Yan P (2021) A tensor-network-based big data fusion framework for cyber–physical–social systems (cpss). Inf Fusion 76:337–354
Wang P, Yang LT, Peng Y, Li J, Xie X (2019) m2t2: the multivariate multistep transition tensor for user mobility pattern prediction. IEEE Trans Netw Sci Eng 7(2):907–917
Kolda TG, Bader BW, Kenny JP (2005) Higher-order web link analysis using multilinear algebra. In: Fifth IEEE international conference on data mining (ICDM’05), IEEE, p 8
Sun J-T, Zeng H-J, Liu H, Lu Y, Chen Z (2005) Cubesvd: a novel approach to personalized web search. In: Proceedings of the 14th international conference on world wide web, pp 382–390
Agrawal R, Golshan B, Papalexakis E (2015) A study of distinctiveness in web results of two search engines. In: Proceedings of the 24th international conference on world wide web, pp 267–273
Liu J, Musialski P, Wonka P, Ye J (2012) Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 35(1):208–220
Vasilescu MAO, Terzopoulos D (2002) Multilinear analysis of image ensembles: tensorfaces. In: European conference on computer vision, Springer, pp 447–460
Tao D, Song M, Li X, Shen J, Sun J, Wu X, Faloutsos C, Maybank SJ (2008) Bayesian tensor approach for 3-d face modeling. IEEE Trans Circ Syst Video Technol 18(10):1397–1410
Wu P-L, Zhao X-L, Ding M, Zheng Y-B, Cui L-B, Huang T-Z (2023) Tensor ring decomposition-based model with interpretable gradient factors regularization for tensor completion. Knowl-Based Syst 259:110094
Du S, Xiao Q, Shi Y, Cucchiara R, Ma Y (2021) Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion. Neurocomputing 458:204–218
Bai Y, Tezcan J, Cheng Q, Cheng J (2013) A multiway model for predicting earthquake ground motion. In: 2013 14th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing, IEEE, pp 219–224
Acknowledgements
We acknowledge the financial support by the National Natural Science Foundation of China (62172182), the Hunan Provincial Natural Science Foundation of China (2020JJ4489 and 2020JJ4490), A Project Supported by Scientific Research Fund of Hunan Provincial Education Department (19A394), and Huaihua University Double First-Class initiative Applied Characteristic Discipline of Control Science and Engineering.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
There are no conflicts of interest to declare.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Deng, X., Shi, Y. & Yao, D. Theories, algorithms and applications in tensor learning. Appl Intell 53, 20514–20534 (2023). https://doi.org/10.1007/s10489-023-04538-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-023-04538-z