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Robust Reduction of a Multidimensional Indicators

Student: Gegham Vardanyan

Supervisor: Elena R. Goryainova

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 8

Year of Graduation: 2017

This paper is devoted to the optimization of the known method of compression of principal components. The work offers robust estimates that improve the method on noisy data. In addition, criteria for assessing the quality of compression will be formed. All compression methods are implemented by me in the python programming language and tested on different data. A comparative analysis of the methods is carried out. Thus, the object of investigation is the method of principal components. The main goal of the work is the development of new robust methods and a comparative analysis of methods on simulated and real data, and the identification of the advantages of the shortcomings of new methods.

Full text (added May 29, 2017)

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