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undergraduate thesis
Metode redukcije dimenzije visokodimenzionalnih podataka
Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2020. urn:nbn:hr:126:464083
Prusina, Tomislav
Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Applied Mathematics and Computer Science
Computer Science and Machine Learning Research Group

Institutional repository: Repository of School of Applied Mathematics and Informatics

Cite this document

Prusina, T. (2020). Metode redukcije dimenzije visokodimenzionalnih podataka (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:464083

Prusina, Tomislav. "Metode redukcije dimenzije visokodimenzionalnih podataka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2020. https://urn.nsk.hr/urn:nbn:hr:126:464083

Prusina, Tomislav. "Metode redukcije dimenzije visokodimenzionalnih podataka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2020. https://urn.nsk.hr/urn:nbn:hr:126:464083

Prusina, T. (2020). 'Metode redukcije dimenzije visokodimenzionalnih podataka', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 21 January 2025, https://urn.nsk.hr/urn:nbn:hr:126:464083

Prusina T. Metode redukcije dimenzije visokodimenzionalnih podataka [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2020 [cited 2025 January 21] Available at: https://urn.nsk.hr/urn:nbn:hr:126:464083

T. Prusina, "Metode redukcije dimenzije visokodimenzionalnih podataka", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, Osijek, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:126:464083

Metadata
TitleMetode redukcije dimenzije visokodimenzionalnih podataka
Title (english)Dimensionality Reduction Methods for High-Dimensional Data
AuthorTomislav Prusina
MentorDomagoj Matijević (mentor)
GranterJosip Juraj Strossmayer University of Osijek
Department of Mathematics
(Chair of Applied Mathematics and Computer Science)
(Computer Science and Machine Learning Research Group)
Osijek
Defense date and country2020-09-29, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Other Mathematical Disciplines
Abstract
U ovome radu ćemo promatrati podatke u visokodimenzionalnim prostorima. Analiza podataka u takvim prostorima može biti računski i vremenski zahtjevna. Stoga uvodimo pojam redukcije (smanjenja) dimenzije kako bismo mogli lakše i brže provesti analizu te ćemo promatrati sljedeće metode za redukcije dimenzije: • slučajna projekcija, • analiza glavnih komponenti, • skaliranje najmanjih kvadrata. Za svaku metodu ćemo navesti prednosti i nedostatke te ćemo za metodu skaliranja najmanjih kvadrata dodatno proučiti algoritam pod nazivom "skaliranje majorizacijom komplicirane funkcije".
Abstract (english)
In this paper, we will observe data in high-dimensional spaces. Data analysis in such spaces can be a computational and time demanding. Therefore, we introduce the concept of dimensionality reduction so that we can more easily and quickly perform data analysis. We will observe the following three methods for reducing dimensions: • random projection, • principal component analysis, • least-squares scaling. For each method we will list the advantages. Additionaly, for the least-squares scaling method we will study the algorithm: scaling by majorizing a complicated function.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:464083
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
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Created on2020-10-30 08:23:27