Abstract
Probabilistic Latent Semantic Indexing (PLSI) is a statistical technique for automatic document indexing. A novel method is proposed for updating PLSI when new documents arrive. The proposed method adds incrementally the words of any new document in the term-document matrix and derives the updating equations for the probability of terms given the class (i.e. latent) variables and the probability of documents given the latent variables. The performance of the proposed method is compared to that of the folding-in algorithm, which is an inexpensive, but potentially inaccurate updating method. It is demonstrated that the proposed updating algorithm outperforms the folding-in method with respect to the mean squared error between the aforementioned probabilities as they are estimated by the two updating methods and the original non-adaptive PLSI algorithm.
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Kotropoulos, C., Papaioannou, A. (2006). A Novel Updating Scheme for Probabilistic Latent Semantic Indexing. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_16
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DOI: https://doi.org/10.1007/11752912_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34117-8
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