{"type":"ScholarlyArticle","title":"Learning Robust Representations for Data Analytics","authors":[{"name":"Sheng Li","slug":"sheng-li-2","given":"Sheng","family":"Li","affiliation":""}],"abstract":"Learning compact representations from high-dimensional and large-scale data plays an essential role in many real-world applications. However, many existing methods show limited performance when data are contaminated with severe noise. To address this challenge, we have proposed several effective methods to extract robust data representations, such as balanced graphs, discriminative subspaces, and robust dictionaries. In addition, several topics are provided as future work.","doi":null,"url":"https://ijcai.org/proceedings/2016/579","pdf":"https://ijcai.org/proceedings/2016/0579.pdf","markdown":"https://ijcai.org/proceedings/2016/579.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/579","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":579,"pages":{"first":4010,"last":4010},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}