Local topic discovery via boosted ensemble of nonnegative matrix factorization

Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Nonnegative matrix factorization (NMF) has been increasingly popular for topic modeling of largescale 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-theart 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.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4944-4948
Number of pages5
ISBN (Electronic)9780999241103
Publication statusPublished - 2017 Jan 1
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 2017 Aug 192017 Aug 25

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period17/8/1917/8/25

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Suh, S., Choo, J., Lee, J., & Reddy, C. K. (2017). Local topic discovery via boosted ensemble of nonnegative matrix factorization. In C. Sierra (Ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 4944-4948). (IJCAI International Joint Conference on Artificial Intelligence). International Joint Conferences on Artificial Intelligence.