TY - GEN
T1 - Local topic discovery via boosted ensemble of nonnegative matrix factorization
AU - Suh, Sangho
AU - Choo, Jaegul
AU - Lee, Joonseok
AU - Reddy, Chandan K.
N1 - Funding Information:
Acknowledgments. This work was supported in part by the National Science Foundation grants IIS-1707498, IIS-1619028, IIS-1646881 and by Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2016R1C1B2015924). Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of funding agencies.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85031926444&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2017/699
DO - 10.24963/ijcai.2017/699
M3 - Conference contribution
AN - SCOPUS:85031926444
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4944
EP - 4948
BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Y2 - 19 August 2017 through 25 August 2017
ER -