Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF (with the I-divergence objective func-tion) optimize the same objective function, although PLSI and NMF

This paper presents a strategy to generate generic summary of documents using Probabilistic Latent Semantic Indexing. Generally a document contains several topics rather than a single one. Summaries created by human beings tend to cover several topics to give the readers an overall idea about

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF (with the I-divergence objective function) optimize the same objective function, although PLSI and NMF

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF optimize the same objective function, although PLSI and NMF are different algorithms as verified

NMF and PLSI are two state-of-the-art unsupervised learning models in data mining, and both are widely used in many applications. References have shown the equivalence between NMF and PLSI under some conditions. However, a new issue arises here: why can they result in different solutions since they are equivalent? or in other words, their algorithm differences are not studied intensively yet. In this note, we explicitly give the algorithm differences between PLSI and NMF. Importantly, we find that even if starting from the same initializations, NMF and PLSI may converge to different local solutions, and the differences between them are born in the additional constraints in PLSI though NMF and PLSI optimize the same objective function.

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent

Classification and clustering methods for documents by probabilistic latent semantic indexing model

results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries