Topic modeling for large-scale text data
This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiment...
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| Veröffentlicht in: | Frontiers of information technology & electronic engineering 2015-06, Vol.16 (6), p.457-465 |
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| container_title | Frontiers of information technology & electronic engineering |
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| creator | Li, Xi-ming Ouyang, Ji-hong Lu, You |
| description | This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance. |
| doi_str_mv | 10.1631/FITEE.1400352 |
| format | Article |
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| identifier | ISSN: 2095-9184 |
| ispartof | Frontiers of information technology & electronic engineering, 2015-06, Vol.16 (6), p.457-465 |
| issn | 2095-9184 2095-9230 |
| language | eng |
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| source | ProQuest Central UK/Ireland; Alma/SFX Local Collection; SpringerLink Journals - AutoHoldings; ProQuest Central |
| subjects | Algorithms Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Convergence Electrical Engineering Electronics and Microelectronics Inference Instrumentation Networks |
| title | Topic modeling for large-scale text data |
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