Szczegóły publikacji
Opis bibliograficzny
A study of parallel techniques for dimensionality reduction and its impact on the quality of text processing algorithms / Marcin PIETROŃ, Maciej WIELGOSZ, Michał KARWATOWSKI, Kazimierz WIATR // Measurement, Automation, Monitoring / Stowarzyszenie Inżynierów i Techników Mechaników Polskich. Sekcja Metrologii, Polskie Stowarzyszenie Pomiarów Automatyki i Robotyki POLSPAR ; ISSN 2450-2855. — Tytuł poprz.: Pomiary, Automatyka, Kontrola ; ISSN: 0032-4140. — 2015 — vol. 61 no. 7, s. 352–354. — Bibliogr. s. 353–354, Abstr.
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 94954 |
|---|---|
| Data dodania do BaDAP | 2016-01-27 |
| Rok publikacji | 2015 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Measurement Automation and Monitoring |
Abstract
The presented algorithms employ the Vector Space Model (VSM) and its enhancements such as TFIDF (Term Frequency Inverse Document Frequency) with Singular Value Decomposition (SVD). TFIDF were applied to emphasize the important features of documents and SVD was used to reduce the analysis space. Consequently, a series of experiments were conducted. They revealed important properties of the algorithms and their accuracy. The accuracy of the algorithms was estimated in terms of their ability to match the human classification of the subject. For unsupervised algorithms the entropy was used as a quality evaluation measure. The combination of VSM, TFIDF, and SVD came out to be the best performing unsupervised algorithm with entropy of 0.16.