The aim of latent semantic indexing (LSI) is to uncover the relationships between terms, hidden concepts, and documents. LSI uses the matrix factorization technique known as singu...
A dual probability model is constructed for the Latent Semantic Indexing LSI using the cosine similarity measure. Both the document-document similarity matrix and the term-term ...
Latent Semantic Indexing (LSI) is a well established and effective framework for conceptual information retrieval. In traditional implementations of LSI the semantic structure of...
Latent Semantic Indexing (LSI) is commonly used to match queries to documents in information retrieval applications. LSI has been shown to improve retrieval performance for some, ...