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undergraduate thesis
Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka
2024. urn:nbn:hr:126:007028
Vinković, Ivana
Josip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics

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Vinković, I. (2024). Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:007028

Vinković, Ivana. "Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:007028

Vinković, Ivana. "Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:007028

Vinković, I. (2024). 'Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, accessed 12 November 2024, https://urn.nsk.hr/urn:nbn:hr:126:007028

Vinković I. Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics; 2024 [cited 2024 November 12] Available at: https://urn.nsk.hr/urn:nbn:hr:126:007028

I. Vinković, "Neke linearne metode redukcije dimenzije visokodimenzionalnih podataka", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:126:007028

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Metadata
TitleNeke linearne metode redukcije dimenzije visokodimenzionalnih podataka
Title (english)Some Linear Methods For Dimensionality Reduction Of High-dimensional Data
AuthorIvana Vinković
MentorDomagoj Matijević (mentor)
GranterJosip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics
Osijek
Defense date and country2024-10-24, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Mathematical Logic and Accounting
Abstract
U ovom radu fokusirat ćemo se na metode za redukciju dimenzije visokodimenzionalnih podataka s posebnim naglaskom na analizu glavnih komponenti (PCA) i dekompoziciju na singularne vrijednosti (SVD). Istrazit ćemo optimalan odabir broja dimenzija u nižedimenzionalnom prostoru kako bi se očuvala ključna struktura i značajke podataka. Navedene metode koriste se za smanjenje dimenzije i očuvanje sto većeg broja informacija, što omogućava njihovu primjenu u kompresiji podataka, vizualizaciji, strojnom učenju i prepoznavanju obrazaca.
Abstract (english)
In this paper, we focus on methods for dimensionality reduction of high-dimensional data, with a particular emphasis on Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). We explore the optimal selection of the number of dimensions in the lower-dimensional space to preserve the key structure and features of the data. These methods are used to reduce dimensionality while retaining as much information as possible, enabling their application in data compression, visualization, machine learning, and pattern recognition.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:007028
Study programmeTitle: Bachelor of Mathematics and Computer Science
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) matematike i računarstva (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) matematike i računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-28 12:29:22