Empirical prior latent Dirichlet allocation model
| Autor: | J.O.A. Ayeni, M.A. Adegoke, P.A. Adewole |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Computation Search engine indexing 02 engineering and technology 01 natural sciences Latent Dirichlet allocation Dirichlet distribution Exponential function 010104 statistics & probability symbols.namesake ComputingMethodologies_PATTERNRECOGNITION 020204 information systems Prior probability 0202 electrical engineering electronic engineering information engineering symbols Side information 0101 mathematics latent Dirichlet allocation semantic indexing empirical prior hidden structures Prediction accuracy Algorithm Latent semantic indexing |
| Zdroj: | Nigerian Journal of Technology; Vol 38, No 1 (2019); 223-232 |
| ISSN: | 2467-8821 0331-8443 |
| DOI: | 10.4314/njt.v38i1.27 |
| Popis: | In this study, empirical prior Dirichlet allocation (epLDA) model that uses latent semantic indexing framework to derive the priors required for topics computation from data is presented. The parameters of the priors so obtained are related to the parameters of the conventional LDA model using exponential function. The model was implemented and tested with benchmarked data and it achieves a prediction accuracy of 92.15%. It was observed that the epLDA model consistently outperforms the conventional LDA model on different datasets with an average percentage accuracy of 6.33%; this clearly demonstrates the advantage of using side information obtained from data for the computation of the mixture components. Keywords: latent Dirichlet allocation; semantic indexing; empirical prior; hidden structures; Prediction accuracy |
| Databáze: | OpenAIRE |
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