TY - CHAP
T1 - Latent semantic analysis (LSA)
T2 - Reduction of dimensions
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - After building the vector space model, we can represent and compare any type of objects of our study. Now we can discuss the question whether we can improve the vector space we have built. The importance of this question is related to the fact that the vector space model can have thousands of features, and possibly many of these features are redundant. Is there any way to get rid of the features that are not that important? Latent Semantic Analysis allows constructing new vector space model with smaller number of dimensions.
AB - After building the vector space model, we can represent and compare any type of objects of our study. Now we can discuss the question whether we can improve the vector space we have built. The importance of this question is related to the fact that the vector space model can have thousands of features, and possibly many of these features are redundant. Is there any way to get rid of the features that are not that important? Latent Semantic Analysis allows constructing new vector space model with smaller number of dimensions.
UR - http://www.scopus.com/inward/record.url?scp=85064639669&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-14771-6_4
DO - 10.1007/978-3-030-14771-6_4
M3 - Capítulo
AN - SCOPUS:85064639669
T3 - SpringerBriefs in Computer Science
SP - 17
EP - 19
BT - SpringerBriefs in Computer Science
PB - Springer
ER -