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RESEARCH PRODUCT
TSVD as a Statistical Estimator in the Latent Semantic Analysis Paradigm
Giovanni PilatoGiorgio Vassallosubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniHellinger DistanceLatent semantic analysisComputer sciencebusiness.industryProbabilistic logicEstimatorStatistical modelPattern recognitionComputer Science ApplicationsHuman-Computer Interactiondata-driven modelingData models Semantics Probability distribution Matrix decomposition Computational modeling Probabilistic logicLSASingular value decompositionComputer Science (miscellaneous)Probability distributionTruncation (statistics)Artificial intelligenceHellinger distancebusinessAlgorithmInformation Systemsdescription
The aim of this paper is to present a new point of view that makes it possible to give a statistical interpretation of the traditional latent semantic analysis (LSA) paradigm based on the truncated singular value decomposition (TSVD) technique. We show how the TSVD can be interpreted as a statistical estimator derived from the LSA co-occurrence relationship matrix by mapping probability distributions on Riemanian manifolds. Besides, the quality of the estimator model can be expressed by introducing a figure of merit arising from the Solomonoff approach. This figure of merit takes into account both the adherence to the sample data and the simplicity of the model. In our model, the simplicity parameter of the proposed figure of merit depends on the number of the singular values retained after the truncation process, while the TSVD estimator, according to the Hellinger distance, guarantees the minimal distance between the sample probability distribution and the inferred probabilistic model.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-06-01 | IEEE Transactions on Emerging Topics in Computing |