{"type":"ScholarlyArticle","title":"Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization","authors":[{"name":"Mehrnaz Najafi","slug":"mehrnaz-najafi","given":"Mehrnaz","family":"Najafi","affiliation":"Department of Computer Science , University of Illinois at Chicago , Chicago , IL , USA"},{"name":"Lifang He","slug":"lifang-he","given":"Lifang","family":"He","affiliation":"Department of Biostatistics and Epidemiology , University of Pennsylvania , Philadelphia , PA , USA"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Department of Computer Science , University of Illinois at Chicago , Chicago , IL , USA"}],"abstract":"With the increasing popularity of streaming tensor data such as videos and audios, tensor factorization and completion have attracted much attention recently in this area. Existing work usually assume that streaming tensors only grow in one mode. However, in many real-world scenarios, tensors may grow in multiple modes (or dimensions), i.e., multi-aspect streaming tensors. Standard streaming methods cannot directly handle this type of data elegantly. Moreover, due to inevitable system errors, data may be contaminated by outliers, which cause significant deviations from real data values and make such research particularly challenging. In this paper, we propose a novel method for Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization (OR-MSTC), which is a technique capable of dealing with missing values and outliers in multi-aspect streaming tensor data. The key idea is to decompose the tensor structure into an underlying low-rank clean tensor and a structured-sparse error (outlier) tensor, along with a weighting tensor to mask missing data. We also develop an efficient algorithm to solve the non-convex and non-smooth optimization problem of OR-MSTC. Experimental results on various real-world datasets show the superiority of the proposed method over the baselines and its robustness against outliers.","doi":"10.24963/ijcai.2019/442","url":"https://ijcai.org/proceedings/2019/442","pdf":"https://ijcai.org/proceedings/2019/0442.pdf","markdown":"https://ijcai.org/proceedings/2019/442.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/442","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":442,"pages":{"first":3187,"last":3194},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Balcan and Zhang, 2016] Maria-Florina Balcan and Hongyang Zhang. 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