Results 1 - 10
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1,237
Probabilistic Latent Semantic Indexing
, 1999
"... 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 ..."
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Cited by 1225 (10 self)
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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
On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing
- Comput. Stat. Data Anal
, 2008
"... 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 a ..."
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Cited by 50 (4 self)
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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
Generic text summarization using probabilistic latent semantic indexing
- In Proceedings of IJCNLP
, 2008
"... 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 the or ..."
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Cited by 5 (0 self)
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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
On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing
, 2008
"... 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 ar ..."
Abstract
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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
Efficient Probabilistic Latent Semantic Indexing using Graphics Processing Unit
"... Abstract In this paper, we attempt to accelerate the Probabilistic Latent Indexing (PLSI) exploiting the high parallelism of Graphic Processing Unit (GPU). Our proposal is composed of three methods. The first method is to accelerate the Expectation-Maximization (EM) computation by applying GPGPU ma ..."
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Abstract In this paper, we attempt to accelerate the Probabilistic Latent Indexing (PLSI) exploiting the high parallelism of Graphic Processing Unit (GPU). Our proposal is composed of three methods. The first method is to accelerate the Expectation-Maximization (EM) computation by applying GPGPU
Classification and clustering methods for documents by probabilistic latent semantic indexing model
, 2006
"... ..."
Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence, Chi-square Statistic, and a Hybrid Method
"... 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 by exper ..."
Abstract
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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
Note on Algorithm Differences Between Nonnegative Matrix Factorization And Probabilistic Latent Semantic Indexing 1 Zhong-Yuan Zhang,
"... 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 ..."
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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.
Unsupervised Learning by Probabilistic Latent Semantic Analysis
- Machine Learning
, 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
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Cited by 618 (4 self)
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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.
Probabilistic Latent Semantic Analysis
- In Proc. of Uncertainty in Artificial Intelligence, UAI’99
, 1999
"... 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 Sema ..."
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Cited by 771 (9 self)
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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
Results 1 - 10
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1,237