PLSA efficiency improvement based on initialization and approximation
| Autor: | Avanesov, V., Kozlov, I. |
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| Jazyk: | ruština |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Вестник Новгородского государственного университета им. Ярослава Мудрого. |
| ISSN: | 2076-8052 |
| Popis: | Probabilistic Latent Semantic Analysis (PLSA) is an effective technique for information retrieval, but it has a serious drawback: it consumes a huge amount of computational resources, so it is hard to train this model on a large collection of documents. The aim of this paper is to improve time efficiency of the training algorithm. Two different approaches are explored: one is based on efficient finding of an appropriate initial approximation; the idea of another is that for the most of collection topics may be extracted from relatively small fraction of the data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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