지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.7
- 수록면
- 883 - 890 (8page)
이용수
초록· 키워드
오류제보하기
In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. PROPOSED WORK
4. EXPERIMENTAL RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCE
참고문헌 (17)
참고문헌 신청
T. Yang, J. Torget, and R. Mihalcea, “Topic Modeling on Historical Newspapers,” Proceeding of the Association for Computational Linguistics: Human Language Technologies Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 96-104, 2011.
M. Lamba, “Mapping of Topics in DESIDOC Journal of Library and Information Technology, India: A Study,” Scientometrics, Vol. 120, No 20, pp. 477-505, 2019.
Y. Zaho and Y. Cen, Data Mining Applications with R, Academic Press, Cambridge, Massachusetts, 2013.
D. Blei, “Probabilistic Topic Models,” Communications of ACM, Vol. 55, No. 4, pp. 77-84, 2012.
D. Lee and H. Seung, “Learning the Parts of Objects by Non-negative Matrix Factorization,” Nature, Vol. 401, pp. 788-791, 1999.
M. Lamba, “Mapping of Topics in DESIDOC Journal of Library and Information Technology, India: A Study,” Scientometrics, Vol. 120, No 20, pp. 477-505, 2019.
Y. Zaho and Y. Cen, Data Mining Applications with R, Academic Press, Cambridge, Massachusetts, 2013.
D. Blei, “Probabilistic Topic Models,” Communications of ACM, Vol. 55, No. 4, pp. 77-84, 2012.
D. Lee and H. Seung, “Learning the Parts of Objects by Non-negative Matrix Factorization,” Nature, Vol. 401, pp. 788-791, 1999.
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UCI(KEPA) : I410-ECN-0101-2020-004-001097110