{"type":"ScholarlyArticle","title":"Local Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization","authors":[{"name":"Sangho Suh","slug":"sangho-suh","given":"Sangho","family":"Suh","affiliation":"Korea University Seoul , South Korea; Jaegul Choo Korea University Seoul , South Korea"},{"name":"Jaegul Choo","slug":"jaegul-choo","given":"Jaegul","family":"Choo","affiliation":""},{"name":"Joonseok Lee","slug":"joonseok-lee","given":"Joonseok","family":"Lee","affiliation":"Google Research Mountain View , CA , USA"},{"name":"Chandan K. Reddy","slug":"chandan-k-reddy","given":"Chandan K.","family":"Reddy","affiliation":"Virginia Tech Arlington , VA , USA"}],"abstract":"Nonnegative matrix factorization (NMF) has been increasingly popular for topic modeling of large-scale documents. However, the resulting topics often represent only general, thus redundant information about the data rather than minor, but potentially meaningful information to users. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model to successively perform NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. The novelty of our method lies in the fact that it utilizes the residual matrix inspired by a state-of-the-art gradient boosting model and applies a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users.","doi":"10.24963/ijcai.2017/699","url":"https://ijcai.org/proceedings/2017/699","pdf":"https://ijcai.org/proceedings/2017/0699.pdf","markdown":"https://ijcai.org/proceedings/2017/699.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/699","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":699,"pages":{"first":4944,"last":4948},"keywords":["Artificial Intelligence: other"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2003] David M. 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