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Regular and semi-regular solids in higher dimensions
Pekař, Vojtěch ; Surynková, Petra (advisor) ; Hromadová, Jana (referee)
This thesis deals with multidimensional objects, which are known as Platonic and Archimedean solids in common euclidean space. Although we describe especially four-dimensional figures and their relations with lesser grade, this text is formulated in such a way, that includes even different dimensions, if it is possible in particular instances. There exist a few works about this and similar topics in foreign, but usually they require a little basics of algebra teaching at university. Our approach uses methods similar to these, which are normally teaching in descriptive geometry and therefore includes a large number of pictures. The matter is therefore available to secondary school students, who want to increase their space imagination.
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Efficient implementation of dimension reduction methods for high-dimensional statistics
Pekař, Vojtěch ; Duintjer Tebbens, Erik Jurjen (advisor) ; Hnětynková, Iveta (referee)
The main goal of our thesis is to make the implementation of a classification method called linear discriminant analysis more efficient. It is a model of multivariate statistics which, given samples and their membership to given groups, attempts to determine the group of a new sample. We focus especially on the high-dimensional case, meaning that the number of variables is higher than number of samples and the problem leads to a singular covariance matrix. If the number of variables is too high, it can be practically impossible to use the common methods because of the high computational cost. Therefore, we look at the topic from the perspective of numerical linear algebra and we rearrange the obtained tasks to their equivalent formulation with much lower dimension. We offer new ways of solution, provide examples of particular algorithms and discuss their efficiency. Powered by TCPDF (www.tcpdf.org)
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Efficient implementation of dimension reduction methods for high-dimensional statistics
Pekař, Vojtěch ; Duintjer Tebbens, Erik Jurjen (advisor) ; Hnětynková, Iveta (referee)
The main goal of our thesis is to make the implementation of a classification method called linear discriminant analysis more efficient. It is a model of multivariate statistics which, given samples and their membership to given groups, attempts to determine the group of a new sample. We focus especially on the high-dimensional case, meaning that the number of variables is higher than number of samples and the problem leads to a singular covariance matrix. If the number of variables is too high, it can be practically impossible to use the common methods because of the high computational cost. Therefore, we look at the topic from the perspective of numerical linear algebra and we rearrange the obtained tasks to their equivalent formulation with much lower dimension. We offer new ways of solution, provide examples of particular algorithms and discuss their efficiency. Powered by TCPDF (www.tcpdf.org)
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Regular and semi-regular solids in higher dimensions
Pekař, Vojtěch ; Surynková, Petra (advisor) ; Hromadová, Jana (referee)
This thesis deals with multidimensional objects, which are known as Platonic and Archimedean solids in common euclidean space. Although we describe especially four-dimensional figures and their relations with lesser grade, this text is formulated in such a way, that includes even different dimensions, if it is possible in particular instances. There exist a few works about this and similar topics in foreign, but usually they require a little basics of algebra teaching at university. Our approach uses methods similar to these, which are normally teaching in descriptive geometry and therefore includes a large number of pictures. The matter is therefore available to secondary school students, who want to increase their space imagination.
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