# Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

arXiv: https://arxiv.org/abs/2404.04403 · HTML: https://mlsanity.com/paper/2404.04403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Jointly Contrastive Representation Learning on Road Network and Trajectory](https://mlsanity.com/paper/2209.06389/) (2022)
- [Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction](https://mlsanity.com/paper/1912.05693/) (2019)
- [Tensor-on-tensor regression](https://mlsanity.com/paper/1701.01037/) (2017)
- [Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile](https://mlsanity.com/paper/2004.11022/) (2020)
- [A novel approach for fusion of heterogeneous sources of data](https://mlsanity.com/paper/1803.00138/) (2018)
- [Dynamic Tensor Clustering](https://mlsanity.com/paper/1708.07259/) (2017)
- [Robust Subspace Clustering with Compressed Data](https://mlsanity.com/paper/1803.11305/) (2018)
- [Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning](https://mlsanity.com/paper/1804.03797/) (2018)
- [Robust Tensor Recovery with Fiber Outliers for Traffic Events](https://mlsanity.com/paper/1908.10198/) (2019)
- [Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee](https://mlsanity.com/paper/2203.16328/) (2022)
- [Robust Low-rank Tensor Decomposition with the operatornameL2 Criterion](https://mlsanity.com/paper/2208.11806/) (2022)
- [Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction](https://mlsanity.com/paper/2109.09317/) (2021)
- [Rapid Detection of Hot-spots via Tensor Decomposition with applications to Crime Rate Data](https://mlsanity.com/paper/2004.11710/) (2020)
- [Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data](https://mlsanity.com/paper/2010.12633/) (2020)

## Built on by (most cited first, 5 of 5)

- [Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer](https://mlsanity.com/paper/2407.18554/) (2024)
- [Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market](https://mlsanity.com/paper/2407.10878/) (2024)
- [Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates](https://mlsanity.com/paper/2407.11180/) (2024)
- [Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture](https://mlsanity.com/paper/2407.21068/) (2024)
- [Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast](https://mlsanity.com/paper/2409.15200/) (2024)

## Abstract (arXiv)

Tensor clustering has become an important topic, specifically in spatio-temporal modeling, due to its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of the day or day of the week). Our motivating example is from subway passenger flow modeling, where similarities between stations are commonly found. However, the challenges lie in the innate high-dimensionality of tensors and also the potential existence of anomalies. This is because the three tasks, i.e., dimension reduction, clustering, and anomaly decomposition, are inter-correlated to each other, and treating them in a separate manner will render a suboptimal performance. Thus, in this work, we design a tensor-based subspace clustering and anomaly decomposition technique for simultaneously outlier-robust dimension reduction and clustering for high-dimensional tensors. To achieve this, a novel low-rank robust subspace clustering decomposition model is proposed by combining Tucker decomposition, sparse anomaly decomposition, and subspace clustering. An effective algorithm based on Block Coordinate Descent is proposed to update the parameters. Prudent experiments prove the effectiveness of the proposed framework via the simulation study, with a gain of +25% clustering accuracy than benchmark methods in a hard case. The interrelations of the three tasks are also analyzed via ablation studies, validating the interrelation assumption. Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.