# Streaming data preprocessing via online tensor recovery for large environmental sensor networks

arXiv: https://arxiv.org/abs/2109.00596 · HTML: https://mlsanity.com/paper/2109.00596/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination](https://mlsanity.com/paper/1401.6497/) (2014)
- [Robust Low-rank Tensor Recovery: Models and Algorithms](https://mlsanity.com/paper/1311.6182/) (2013)
- [Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors](https://mlsanity.com/paper/1404.4667/) (2014)
- [Robust low-rank covariance matrix estimation with a general pattern of missing values](https://mlsanity.com/paper/2107.10505/) (2021)
- [Robust Tensor Recovery with Fiber Outliers for Traffic Events](https://mlsanity.com/paper/1908.10198/) (2019)
- [Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers](https://mlsanity.com/paper/2102.08466/) (2021)
- [Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method](https://mlsanity.com/paper/2008.07740/) (2020)
- [Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise](https://mlsanity.com/paper/1809.03550/) (2018)

## Built on by (most cited first, 3 of 3)

- [Streaming data recovery via Bayesian tensor train decomposition](https://mlsanity.com/paper/2302.12148/) (2023)
- [Fast White-Box Adversarial Streaming Without a Random Oracle](https://mlsanity.com/paper/2406.06808/) (2024)
- [A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation](https://mlsanity.com/paper/2511.01267/) (2025)

## Abstract (arXiv)

Measuring the built and natural environment at a fine-grained scale is now possible with low-cost urban environmental sensor networks. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. While many methods exist to automatically correct anomalies and impute missing entries, challenges still exist on data with large spatial-temporal scales and shifting patterns. To address these challenges, we propose an online robust tensor recovery (OLRTR) method to preprocess streaming high-dimensional urban environmental datasets. A small-sized dictionary that captures the underlying patterns of the data is computed and constantly updated with new data. OLRTR enables online recovery for large-scale sensor networks that provide continuous data streams, with a lower computational memory usage compared to offline batch counterparts. In addition, we formulate the objective function so that OLRTR can detect structured outliers, such as faulty readings over a long period of time. We validate OLRTR on a synthetically degraded National Oceanic and Atmospheric Administration temperature dataset, with a recovery error of 0.05, and apply it to the Array of Things city-scale sensor network in Chicago, IL, showing superior results compared with several established online and batch-based low rank decomposition methods.