{"type":"ScholarlyArticle","title":"Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression","authors":[{"name":"Peiyan Li","slug":"peiyan-li","given":"Peiyan","family":"Li","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Honglian Wang","slug":"honglian-wang","given":"Honglian","family":"Wang","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Christian Böhm","slug":"christian-bohm","given":"Christian","family":"Böhm","affiliation":"Ludwig-Maximilians-Universität München"},{"name":"Junming Shao","slug":"junming-shao","given":"Junming","family":"Shao","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"}],"abstract":"Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only a few multi-label stream classification algorithms exist, and they are mainly trained on labeled instances. In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in a semi-supervised setting and is robust to evolving label distributions. Specifically, to capture the high-order label relationship and to build a compact target space for regression, OnSeML compresses the label set into a low-dimensional space by a fixed orthogonal label encoder. Then a locally defined regression function for each incoming instance is obtained with a closed-form solution. Targeting the evolving label distribution problem, we propose an adaptive decoding scheme to adequately integrate newly arriving labeled data. Extensive experiments provide empirical evidence for the effectiveness of our approach.","doi":"10.24963/ijcai.2020/189","url":"https://ijcai.org/proceedings/2020/189","pdf":"https://ijcai.org/proceedings/2020/0189.pdf","markdown":"https://ijcai.org/proceedings/2020/189.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/189","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":189,"pages":{"first":1359,"last":1365},"keywords":["Data Mining: Classification, Semi-Supervised Learning","Data Mining: Mining Data Streams"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25080/file_25080.m3u8","length_sec":289,"provider":"hls"}],"references":[{"position":1,"raw":"[Ahmadi and Kramer, 2018] Zahra Ahmadi and Stefan Kramer. 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