{"type":"ScholarlyArticle","title":"Multi-version Tensor Completion for Time-delayed Spatio-temporal Data","authors":[{"name":"Cheng Qian","slug":"cheng-qian","given":"Cheng","family":"Qian","affiliation":""},{"name":"Nikos Kargas","slug":"nikos-kargas","given":"Nikos","family":"Kargas","affiliation":""},{"name":"Cao Xiao","slug":"cao-xiao","given":"Cao","family":"Xiao","affiliation":""},{"name":"Lucas Glass","slug":"lucas-glass","given":"Lucas","family":"Glass","affiliation":""},{"name":"Nicholas Sidiropoulos","slug":"nicholas-sidiropoulos","given":"Nicholas","family":"Sidiropoulos","affiliation":""},{"name":"Jimeng Sun","slug":"jimeng-sun","given":"Jimeng","family":"Sun","affiliation":""}],"abstract":"Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to  27.2%  lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.","doi":"10.24963/ijcai.2021/400","url":"https://ijcai.org/proceedings/2021/400","pdf":"https://ijcai.org/proceedings/2021/0400.pdf","markdown":"https://ijcai.org/proceedings/2021/400.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/400","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":400,"pages":{"first":2906,"last":2912},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Machine Learning: Time-series; Data Streams","Machine Learning: Unsupervised Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}