### Abstract

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.

Original language | English |
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Title of host publication | SpringerBriefs in Computer Science |

Publisher | Springer |

Pages | 17-19 |

Number of pages | 3 |

DOIs | |

State | Published - 1 Jan 2019 |

### Publication series

Name | SpringerBriefs in Computer Science |
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ISSN (Print) | 2191-5768 |

ISSN (Electronic) | 2191-5776 |

### Fingerprint

### Cite this

*SpringerBriefs in Computer Science*(pp. 17-19). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-3-030-14771-6_4

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*SpringerBriefs in Computer Science.*SpringerBriefs in Computer Science, Springer, pp. 17-19. https://doi.org/10.1007/978-3-030-14771-6_4

**Latent semantic analysis (LSA): Reduction of dimensions.** / Sidorov, Grigori.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Latent semantic analysis (LSA): Reduction of dimensions

AU - Sidorov, Grigori

PY - 2019/1/1

Y1 - 2019/1/1

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 -