Matrix Factorization in Latent Semantic Indexing

2021 ◽  
Author(s):  
Wei Shean Ng ◽  
Wen Kai Adrian Tang
Keyword(s):  
Matrix Factorization ◽  
Latent Semantic Indexing ◽  
Semantic Indexing

scholarly journals A COMPARISON OF METHODS FOR MODIFYING THE PARTIAL SINGULAR VALUE DECOMPOSITION IN LATENT SEMANTIC INDEXING

2006 ◽  
Vol 43 (2) ◽  
Author(s):  
Jane E. Tougas
Keyword(s):  
Singular Value Decomposition ◽  
Matrix Factorization ◽  
Latent Semantic Indexing ◽  
Factorization Method ◽  
Singular Value ◽  
Semantic Indexing ◽  
Partial Singular Value Decomposition ◽  
Base De Données ◽  
Computationally Expensive ◽  
Value Decomposition

The tremendous size of the Internet and modern databases has made efficientsearching and information retrieval (IR) important. Latent semantic indexing (LSI) is an IR method that represents a dataset as a term-document matrix. LSI uses a matrix factorization method known as the partial singular value decomposition (PSVD). Calculating the PSVD of a large term-document matrix is computationally expensive. In a rapidly expanding environment, a term-document matrix is altered often as new documents and terms are added. Recomputing the PSVD of the term-document matrix each time these slight alterations occur can be prohibitively expensive. Folding-in is one method of adding new documents or terms to an LSI database; updating the PSVD of the existing LSI database is another. The folding-in method is computationally inexpensive, but may cause deterioration in the accuracy of the PSVD. The PSVD-updating method is computationally more expensive than the folding-inmethod, but better maintains the accuracy of the PSVD. Folding-up is a new method that combines folding-in and PSVD-updating. Folding-up is faster than either recomputing the PSVD or PSVD-updating, but avoids the degradation in the PSVD that can occur when the folding-in method is used on its own.La taille incroyable d’Internet et des bases de données modernes a fait en sorteque la recherche efficace d’informations est maintenant importante. L’indexation par sémantique latente (ISL) est une méthode de recherche d’informations qui représente un jeu de données comme une matrice document-terme. L’ISL comprend l’utilisation d’une méthode de factorisation matricielle connue sous le nom de décomposition partielle en valeurs singulières (DPVS). Le calcul de la DPVS d’une grande matrice document-terme est coûteux sur le plan des calculs. Dans un environnement en expansion rapide, une matrice document-terme est souvent modifiée à mesure que de nouveaux documents et termes sont ajoutés. Le recalcul de la DPVS de la matrice document-terme chaque fois qu’une légère modification est apportée peut devenir très coûteux. L’intégration (folding-in) est une méthode pour ajouter de nouveaux documents ou termes dans une base de donnée ISL, et la mise à jour de la DPVS de la base de données ISL existante en est une autre. La méthode d’intégration est peu coûteuse sur le plan des calculs, mais elle peut entraîner une perte d’exactitude de la DPVS. La méthode de mise à jour de la DPVS est plus coûteuse sur le plan des calculs, mais elle permet de mieux préserver l’exactitude de la DPVS. La méthode d’intégration et de mise à jour (folding-up) est une nouvelle méthode qui combine l’intégration et la mise à jour de la DPVS. Cette méthode est plus rapide que le recalcul ou la mise à jour de la DPVS, mais elle permet d’éviter la perte d’exactitude de la DPVS qui peut survenir quand seule la méthode d’intégration est utilisée.

Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing

2008 ◽  
Vol 7 (1) ◽  
pp. 182-191 ◽  
Author(s):  
Sebastian Klie ◽  
Lennart Martens ◽  
Juan Antonio Vizcaíno ◽  
Richard Côté ◽  
Phil Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Semantic Indexing ◽  
Semantic Indexing

A Method Based on Support Vector Machine for Feature Selection of Latent Semantic Features

2011 ◽  
Vol 181-182 ◽  
pp. 830-835
Author(s):  
Min Song Li
Keyword(s):  
Support Vector Machine ◽  
Text Categorization ◽  
Latent Semantic Indexing ◽  
Classification Performance ◽  
Compact Representation ◽  
Support Vector ◽  
Semantic Features ◽  
Semantic Indexing ◽  
Feature Extraction Method ◽  
Feature Subspace

Latent Semantic Indexing(LSI) is an effective feature extraction method which can capture the underlying latent semantic structure between words in documents. However, it is probably not the most appropriate for text categorization to use the method to select feature subspace, since the method orders extracted features according to their variance,not the classification power. We proposed a method based on support vector machine to extract features and select a Latent Semantic Indexing that be suited for classification. Experimental results indicate that the method improves classification performance with more compact representation.

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 169-185
Author(s):  
Saida Ishak Boushaki ◽  
Omar Bendjeghaba ◽  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Latent Semantic Indexing ◽  
Research Area ◽  
Semantic Indexing ◽  
Local Optima ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

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