Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Nurretin Dorukhan Sergin,
Jiuyun Hu,
Ziyue Li
et al.
Abstract:Tensor clustering has become an important topic, specifically in spatiotemporal modeling, because of its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of 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, that is, dim… Show more
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