An empirical study of required dimensionality for large-scale latent semantic indexing applications

2008 ◽  
Author(s):  
Roger B. Bradford
Keyword(s):  
Empirical Study ◽  
Large Scale ◽  
Latent Semantic Indexing ◽  
Semantic Indexing

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

Large-scale information retrieval with latent semantic indexing

1997 ◽  
Vol 100 (1-4) ◽  
pp. 105-137 ◽  
Author(s):  
Todd A. Letsche ◽  
Michael W. Berry
Keyword(s):  
Information Retrieval ◽  
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.

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