Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
- URL: http://arxiv.org/abs/2404.04403v1
- Date: Fri, 5 Apr 2024 21:00:43 GMT
- Title: Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
- Authors: Jiuyun Hu, Ziyue Li, Chen Zhang, Fugee Tsung, Hao Yan,
- Abstract summary: In this work, we design a tensor-based subspace and anomaly decomposition technique for simultaneously-robust dimension reduction clustering and anomaly decomposition.
A novel low-rank robust subspace clustering decomposition model is proposed by combining Tucker decomposition, sparse anomaly decomposition, and subspace clustering.
Experiments prove the effectiveness of proposed framework, with a gain of +25% clustering accuracy.
- Score: 19.907314837560907
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- GCC: Generative Calibration Clustering [55.44944397168619]
We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure.
First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples.
Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
arXiv Detail & Related papers (2024-04-14T01:51:11Z) - Sanitized Clustering against Confounding Bias [38.928080236294775]
This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias (SCAB)
SCAB removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure.
Experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance.
arXiv Detail & Related papers (2023-11-02T14:10:14Z) - A Nested Matrix-Tensor Model for Noisy Multi-view Clustering [5.132856740094742]
We propose a nested matrix-tensor model which extends the spiked rank-one tensor model of order three.
We show that our theoretical results allow us to anticipate the exact accuracy of the proposed clustering approach.
Our analysis unveils unexpected and non-trivial phase transition phenomena depending on the model parameters.
arXiv Detail & Related papers (2023-05-31T16:13:46Z) - Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor
Space for Multi-view Clustering [0.0]
We propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering.
Specifically, we adopt the concept factorization to explore the latent cluster-wise representation of each view.
Considering that different tensor singular values associate structural information with unequal importance, we develop a self-weighted tensor Schatten p-norm.
arXiv Detail & Related papers (2023-04-22T15:46:58Z) - Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF)
Our model directly considers the between-view relationship and exploits the between-view complementary information.
In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
arXiv Detail & Related papers (2023-03-29T14:54:19Z) - Truncated tensor Schatten p-norm based approach for spatiotemporal
traffic data imputation with complicated missing patterns [77.34726150561087]
We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers.
Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
arXiv Detail & Related papers (2022-05-19T08:37:56Z) - Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly
Types [60.45942774425782]
We introduce anomaly clustering, whose goal is to group data into coherent clusters of anomaly types.
This is different from anomaly detection, whose goal is to divide anomalies from normal data.
We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods.
arXiv Detail & Related papers (2021-12-21T23:11:33Z) - Correlation Clustering Reconstruction in Semi-Adversarial Models [70.11015369368272]
Correlation Clustering is an important clustering problem with many applications.
We study the reconstruction version of this problem in which one is seeking to reconstruct a latent clustering corrupted by random noise and adversarial modifications.
arXiv Detail & Related papers (2021-08-10T14:46:17Z) - Sparse Generalized Yule-Walker Estimation for Large Spatio-temporal
Autoregressions with an Application to NO2 Satellite Data [0.0]
We consider sparse estimation of a class of high-dimensional models.
We estimate the relationships governing both the spatial and temporal dependence in a fully-driven way by penalizing a set of Yule-Walker equations.
A satellite simulation exercise shows strong finite sample performance compared to competing procedures.
arXiv Detail & Related papers (2021-08-05T21:51:45Z) - Exact Clustering in Tensor Block Model: Statistical Optimality and
Computational Limit [10.8145995157397]
High-order clustering aims to identify heterogeneous substructure in multiway dataset.
Non- computation and nature of the problem poses significant challenges in both statistics and statistics.
arXiv Detail & Related papers (2020-12-18T00:48:27Z) - Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation [105.33409035876691]
This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling.
We design a novel structured tensor low-rank norm tailored to MVSC.
We show that the proposed method outperforms state-of-the-art methods to a significant extent.
arXiv Detail & Related papers (2020-04-30T11:52:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.