1 |
HOFFMAN M D, BLEI D M, WANG C, et al. Stochastic variational inference[J]. Journal of Machine Learning Research, 2013, 14:1303-1347.
|
2 |
REYNOLDS D. Gaussian mixture models[M]// LI S Z, JAIN A K. Encyclopedia of Biometrics. Boston: Springer, 2009:659-663. 10.1007/978-0-387-73003-5_196
|
3 |
BLEI D, CARIN L, DUNSON D. Probabilistic topic models[J]. IEEE Signal Processing Magazine, 2010, 27(6): 55-65.
|
4 |
TERENIN A, SIMPSON D, DRAPER D. Asynchronous Gibbs sampling[C]// Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics. New York: JMLR.org, 2020:144-154.
|
5 |
MOON T K. The expectation-maximization algorithm[J]. IEEE Signal Processing Magazine, 1996, 13(6): 47-60. 10.1109/79.543975
|
6 |
BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
|
7 |
WANG X R, McCALLUM A. Topics over time: a non-Markov continuous-time model of topical trends[C]// Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006: 424-433. 10.1145/1150402.1150450
|
8 |
BLEI D M, LAFFERTY J D. Dynamic topic models[C]// Proceedings of the 23rd International Conference on Machine Learning. New York: ACM, 2006: 113-120. 10.1145/1143844.1143859
|
9 |
IWATA T, WATANABE S, YAMADA T, et al. Topic tracking model for analyzing consumer purchase behavior[C]// Proceedings of the 21st International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc., 2009: 1427-1432.
|
10 |
AMOUALIAN H, CLAUSEL M, GAUSSIER E, et al. Streaming-LDA: a copula-based approach to modeling topic dependencies in document streams[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 695-704. 10.1145/2939672.2939781
|
11 |
ZHAO Y K, LIANG S S, REN Z C, et al. Explainable user clustering in short text streams[C]// Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2016: 155-164. 10.1145/2911451.2911522
|
12 |
HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. 10.1126/science.1127647
|
13 |
XIE J Y, GIRSHICK R, FARHADI A. Unsupervised deep embedding for clustering analysis[C]// Proceedings of the 33rd International Conference on Machine Learning. New York: JMLR.org, 2016: 478-487.
|
14 |
RAIBER F, KURLAND O. Kullback-Leibler divergence revisited[C]// Proceedings of the 2017 ACM SIGIR International Conference on Theory of Information Retrieval. New York: ACM 2017: 117-124. 10.1145/3121050.3121062
|
15 |
BO D Y, WANG X, SHI C, et al. Structural deep clustering network[C]// Proceedings of the Web Conference 2020. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2020: 1400-1410. 10.1145/3366423.3380214
|
16 |
KINGMA D P, WELLING M. Auto-encoding variational Bayes[EB/OL]. (2022-12-10) [2023-02-25].. 10.1561/2200000056
|
17 |
ZHANG D J, SUN Y F, ERIKSSON B, et al. Deep unsupervised clustering using mixture of autoencoders[EB/OL]. (2017-12-26) [2022-09-25]..
|
18 |
JIANG Z X, ZHENG Y, TAN H C, et al. Variational deep embedding: an unsupervised and generative approach to clustering[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2017: 1965-1972. 10.24963/ijcai.2017/273
|
19 |
BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828. 10.1109/tpami.2013.50
|
20 |
侯艳辉,董慧芳,郝敏,等. 基于本体特征的影评细粒度情感分类[J]. 计算机应用, 2020, 40(4): 1074-1078.
|
|
HOU Y H, DONG H F, HAO M, et al. Fine-grained sentiment classification of film reviews based on ontological features[J]. Journal of Computer Applications, 2020, 40(4): 1074-1078.
|
21 |
KRASKOV A, STÖGBAUER H, GRASSBERGER P. Estimating mutual information[J]. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 69(6): No.066138. 10.1103/physreve.69.066138
|
22 |
JONKER R, VOLGENANT T. Improving the Hungarian assignment algorithm[J]. Operations Research Letters, 1986, 5(4): 171-175. 10.1016/0167-6377(86)90073-8
|