mlsanityData to 14 Oct 2026

Tensor Completion Algorithms in Big Data Analytics

arXiv 1711.10105 ↗First posted 28 Nov 2017stat.ML cs.AI cs.LGcited by 48

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Abstract (arXiv)

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 12; 2022: 4; 2023: 7; 2024: 5; 2025: 10; 2026: 10.

48 papers.

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  1. 2021Implicit Regularization in Tensor Factorizationcited by 44
  2. 2021Provable Tensor-Train Format Tensor Completion by Riemannian Optimizationcited by 17
  3. 2023SlimmeRF: Slimmable Radiance Fieldscited by 15
  4. 2021Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outlierscited by 6
  5. 2021Implicit Regularization in Deep Tensor Factorizationcited by 5
  6. 2021Covariate-assisted Sparse Tensor Completioncited by 4
  7. 2021MTC: Multiresolution Tensor Completion from Partial and Coarse Observationscited by 4
  8. 2021Nonnegative Tensor Completion via Integer Optimizationcited by 4
  9. 2025Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approachcited by 4
  10. 2021Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Datacited by 3
  11. 2022Implicit Regularization with Polynomial Growth in Deep Tensor Factorizationcited by 3
  12. 2024Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AIcited by 3
36 more
  1. 2021Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computationcited by 2
  2. 2022TTML: tensor trains for general supervised machine learningcited by 2
  3. 2023Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputationcited by 2
  4. 2021Tensor completion using geodesics on Segre manifoldscited by 1
  5. 2022Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularizationcited by 1
  6. 2022Accelerated Nonnegative Tensor Completion via Integer Programmingcited by 1
  7. 2023A generalizable framework for low-rank tensor completion with numerical priorscited by 1
  8. 2023Image Reconstruction using Superpixel Clustering and Tensor Completioncited by 1
  9. 2025Knowledge Graph Completion by Intermediate Variables Regularizationcited by 1
  10. 2025Tensor Train Completion from Fiberwise Observations Along a Single Modecited by 1
  11. 2026Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularizationcited by 1
  12. 2021Applying Differential Privacy to Tensor Completioncited by 0
  13. 2021"Sparse + Low-Rank'' Tensor Completion Approach for Recovering Images and Videoscited by 0
  14. 2023Tensor PCA from basis in tensor spacecited by 0
  15. 2023Scalable and Robust Tensor Ring Decomposition for Large-scale Datacited by 0
  16. 2023Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebracited by 0
  17. 2024Tensor Completion via Integer Optimizationcited by 0
  18. 2024A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILMcited by 0
  19. 2024Simple and Nearly-Optimal Sampling for Rank-1 Tensor Completion via Gauss-Jordancited by 0
  20. 2024A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Modelscited by 0
  21. 2025Fast Tensor Completion via Approximate Richardson Iterationcited by 0
  22. 2025`Generalization is hallucination' through the lens of tensor completionscited by 0
  23. 2025Global and Local Structure Learning for Sparse Tensor Completioncited by 0
  24. 2025Completion of the DrugMatrix Toxicogenomics Database using 3-Dimensional Tensorscited by 0
  25. 2025LRTuckerRep: Low-rank Tucker Representation Model for Multi-dimensional Data Completioncited by 0
  26. 2025Kernel Regression of Multi-Way Data via Tensor Trains with Hadamard Overparametrization: The Dynamic Graph Flow Casecited by 0
  27. 2025Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methodscited by 0
  28. 2026Combinatorial Bandit Bayesian Optimization for Tensor Outputscited by 0
  29. 2026Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computationcited by 0
  30. 2026DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networkscited by 0
  31. 2026Low Rank Tensor Completion via Adaptive ADMMcited by 0
  32. 2026Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Datacited by 0
  33. 2026Robust Neural Tucker Factorization with Bias Correction and Adaptive Initializationcited by 0
  34. 2026Exponential-Family Tensor Completion via Nonconvex Dual Total-Variation Regularizationcited by 0
  35. 2026Kernel Regression with Tensor Trains and Hadamard Overparameterizationcited by 0
  36. 2026CNA: An AI-Oriented Comprehensive Normalized Assessment for Healthy Status and Application to Optimize RRT Strategies by Reinforcement Learningcited by 0