期刊名称：IEEE Transactions on Emerging Topics in Computing
印刷版ISSN：2168-6750
出版年度：2015
卷号：3
期号：2
页码：185-192
DOI：10.1109/TETC.2014.2385594
出版社：IEEE Publishing
摘要：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.
