Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis
Yükleniyor...
Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The latent semantic analysis (LSA) is a mathematical/statistical way of discovering hidden concepts between terms and documents or within a document collection (i.e., a large corpus of text). Each document of the corpus and terms are expressed as a vector with elements corresponding to these concepts to form a term-document matrix. Then, the LSA uses a low-rank approximation to the term-document matrix in order to remove irrelevant information, to extract more important relations, and to reduce the computational time. The irrelevant information is called as noise and does not have a noteworthy effect on the meaning of the document collection. This is an essential step in the LSA. The singular value decomposition (SVD) has been the main tool obtaining the low-rank approximation in the LSA. Since the document collection is dynamic (i.e., the term-document matrix is subject to repeated updates), we need to renew the approximation. This can be done via recomputing the SVD or updating the SVD. However, the computational time of recomputing or updating the SVD of the term-document matrix is very high when adding new terms and/or documents to preexisting document collection. Therefore, this issue opened the door of using other matrix decompositions for the LSA as ULV- and URV-based decompositions. This study shows that the truncated ULV decomposition (TULVD) is a good alternative to the SVD in the LSA modeling.
Açıklama
Horasan, Fahrettin/0000-0003-4554-9083; Erbay, Hasan/0000-0002-7555-541X; Deniz, Emre/0000-0003-0546-4229; Varcin, Fatih/0000-0002-5100-3012
Anahtar Kelimeler
Kaynak
Scientific Programming
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
2019
Sayı
Künye
Fahrettin Horasan, Hasan Erbay, Fatih Varçın, Emre Deniz, "Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis", Scientific Programming, vol. 2019, 1-12.
